Research responding to this call for a better understanding of IGD is still at a formative stage, and there are active debates surrounding it. There is a growing literature that suggests there is a basis to expect that excessive or problematic gaming may be related to lower health, though findings in this area are mixed. Some argue for a theoretical framing akin to a substance abuse disorder (i.e. where gaming is considered to be inherently addictive), while others frame Internet-based gaming as a self-regulatory challenge for individuals.

In their article “A prospective study of the motivational and health dynamics of Internet Gaming Disorder“, Netta Weinstein, the OII’s Andrew Przybylski, and Kou Murayama address this gap in the literature by linking self-regulation and Internet Gaming Disorder research. Drawing on a representative sample of 5,777 American adults they examine how problematic gaming emerges from a state of individual “dysregulation” and how it predicts health — finding no evidence directly linking IGD to health over time.

This negative finding indicates that IGD may not, in itself, be robustly associated with important clinical outcomes. As such, it may be premature to invest in management of IGD using the same kinds of approaches taken in response to substance-based addiction disorders. Further, the findings suggests that more high-quality evidence regarding clinical and behavioural effects is needed before concluding that IGD is a legitimate candidate for inclusion in future revisions of the Diagnostic and Statistical Manual of Mental Disorders.

We caught up with Andy to explore the implications of the study:

Ed: To ask a blunt question upfront: do you feel that Internet Gaming Disorder is a valid psychiatric condition (and that “games can cause problems”)? Or is it still too early to say?

Andy: No, it is not. It’s difficult to overstate how sceptical the public should be of researchers who claim, and communicate their research, as if Internet addiction, gaming addiction, or Internet gaming disorder (IGD) are recognized psychiatric disorders. The fact of the matter is that American psychiatrists working on the most recent revision of the Diagnostic and Statistical Manual of Mental Disorders (DSM5) highlighted that problematic online play was a topic they were interested in learning more about. These concerns are highlighted in Section III of the DSM5 (entitled “Emerging Measures and Models”). For those interested in this debate see this position paper.

Ed: Internet gaming seems like quite a specific activity to worry about: how does it differ from things like offline video games, online gambling and casino games; or indeed the various “problematic” uses of the Internet that lead to people admitting themselves to digital detox camps?

Andy: In some ways computer games, and Internet ones, are distinct from other activities. They are frequently updated to meet players expectations and some business models of games, such as pay-to-play are explicitly designed to target high engagement players to spend real money for in-game advantages. Detox camps are very worrying to me as a scientist because they have no scientific basis, many of those who run them have financial conflicts of interest when they comment in the press, and there have been a number of deaths at these facilities.

Ed: You say there are two schools of thought: that if IGD is indeed a valid condition, that it should be framed as an addiction, i.e. that there’s something inherently addictive about certain games. Alternatively, that it should be framed as a self-regulatory challenge, relating to an individual’s self-control. I guess intuitively it might involve a bit of both: online environments can be very persuasive, and some people are easily persuaded?

Andy: Indeed it could be. As researchers mainly interested in self-regulation we’re most interested in gaming as one of many activities that can be successfully (or unsuccessfully) integrated into everyday life. Unfortunately we don’t know much for sure about whether there is something inherently addictive about games because the research literature is based largely on inferences based on correlational data, drawn from convenience samples, with post-hoc analyses. Because the evidence base is of such low quality most of the published findings (i.e. correlations/factor analyses) regarding gaming addiction supporting it as valid condition likely suffer from the Texas Sharpshooter Fallacy.

Ed: Did you examine the question of whether online games may trigger things like anxiety, depression, violence, isolation etc. — or whether these conditions (if pre-existing) might influence the development of IGD?

Andy: Well, our modelling focused on the links between Internet Gaming Disorder, health (mental, physical, and social), and motivational factors (feeling competent, choiceful, and a sense of belonging) examined at two time points six months apart. We found that those who had their motivational needs met at the start of the study were more likely to have higher levels of health six months later and were less likely to say they experienced some of the symptoms of Internet Gaming Disorder.

Though there was no direct link between Internet Gaming Disorder and health six months later, we perform an exploratory analysis (one we did not pre-register) and found an indirect link between Internet Gaming Disorder and health by way of motivational factors. In other words, Internet Gaming Disorder was linked to lower levels of feeling competent, choiceful, and connected, which was in turn linked to lower levels of health.

Ed: All games are different. How would a clinician identify if someone was genuinely “addicted” to a particular game — there would presumably have to be game-by-game ratings of their addictive potential (like there are with drugs). How would anyone find the time to do that? Or would diagnosis focus more on the individual’s behaviour, rather than what games they play? I suppose this goes back to the question of whether “some games are addictive” or whether just “some people have poor self-control”?

Andy: No one knows. In fact, the APA doesn’t define what “Internet Games” are. In our research we define ask participants to define it for themselves by “Think[ing] about the Internet games you may play on Facebook (e.g. Farmville), Tablet/Smartphones (e.g. Candy Crush), or Computer/Consoles (e.g. Minecraft).” It’s very difficult to overstate how suboptimal this state of affairs is from a scientific perspective.

Ed: Is it odd that it was the APA’s Substance-Related Disorders Work Group that has called for research into IGD? Are “Internet Games” unique in being classed as a substance, or are there other information based-behaviours that fall under the group’s remit?

Andy: Yes it’s very odd. Our research group is not privy to these discussions but my understanding is that a range of behaviours and other technology-related activities, such as general Internet use have been discussed.

Ed: A huge amount of money must be spent on developing and refining these games, i.e. to get people to spend as much time (and money) as possible playing them. Are academics (and clinicians) always going to be playing catch-up to industry?

Andy: I’m not sure that there is one answer to this. One useful way to think of online games is using the example of a gym. Gyms are most profitable when many people are paying for (and don’t cancel) their memberships but owners can still maintain a small footprint. The world’s most successful gym might be a one square meter facility, with seven billion members but no one ever goes. Many online games are like this, some costs scale nicely, but others have high costs, like servers, community management, upkeep, and power. There are many studying the addictive potential of games but because they constantly reinvent the wheel by creating duplicate survey instruments (there are literally dozens that are only used once or a couple of times) very little of real-world relevance is ever learned or transmitted to the public.

Ed: It can’t be trivial to admit another condition into the Diagnostic and Statistical Manual of Mental Disorders (DSM-5)? Presumably there must be firm (reproducible) evidence that it is a (persistent) problem for certain people, with a specific (identifiable) cause — given it could presumably be admitted in courts as a mitigating condition, and possibly also have implications for health insurance and health policy? What are the wider implications if it does end up being admitted to the DSM-5?

Andy: It is very serious stuff. Opening the door to pathologizing one of the world’s most popular recreational activities risks stigmatizing hundreds of millions of people and shifting resources in an already overstretched mental health systems over the breaking point.

Ed: You note that your study followed a “pre-registered analysis plan” — what does that mean?

Andy: We’ve discussed the wider problems in social, psychological, and medical science before. But basically, preregistration, and Registered Reports provide scientists a way to record their hypotheses in advance of data collection. This improves the quality of the inferences researchers draw from experiments and large-scale social data science. In this study, and also in our other work, we recorded our sampling plan, our analysis plan, and our materials before we collected our data.

Ed: And finally: what follow up studies are you planning?

Andy: We are now conducting a series of studies investigating problematic play in younger participants with a focus on child-caregiver dynamics.

Read the full article: Weinstein N, Przybylski AK, Murayama K. (2017) A prospective study of the motivational and health dynamics of Internet Gaming Disorder. PeerJ 5:e3838 https://doi.org/10.7717/peerj.3838

Additional peer-reviewed articles in this area by Andy include:

Przybylski, A.K. & Weinstein N. (2017). A Large-Scale Test of the Goldilocks Hypothesis: Quantifying the Relations Between Digital Screens and the Mental Well-Being of Adolescents. Psychological Science. DOI: 10.1177/0956797616678438.

Przybylski, A. K., Weinstein, N., & Murayama, K. (2016). Internet Gaming Disorder: Investigating the Clinical Relevance of a New Phenomenon. American Journal of Psychiatry. DOI: 10.1176/appi.ajp.2016.16020224.

Przybylski, A. K. (2016). Mischievous responding in Internet Gaming Disorder research. PeerJ, 4, e2401. https://doi.org/10.7717/peerj.2401

For more on the ongoing “crisis in psychology” and how pre-registration of studies might offer a solution, see this discussion with Andy and Malte Elson: Psychology is in crisis, and here’s how to fix it.

Andy Przybylski was talking to blog editor David Sutcliffe.

Children have never been afforded the full sovereignty of adulthood (Cunningham, 2009) but both these concepts suggest children have become the points of application for new forms of power that have emerged from the digitisation of society. The most dominant form of this power is called “platform capitalism” (Srnicek, 2016). As a result of platform capitalism’s success, there has never been a stronger association between data, young people’s private lives, their relationships with friends and family, their life at school, and the broader political economy. In this post I will define platform capitalism, outline why it has come to dominate children’s relationship to the internet and suggest two reasons in particular why this is problematic.

Children predominantly experience the Internet through platforms

‘At the most general level, platforms are digital infrastructures that enable two or more groups to interact. They therefore position themselves as intermediaries that bring together different users: customers, advertisers, service providers, producers, suppliers, and even physical objects’ (Srnicek 2016, p43). Examples of platforms capitalism include the technology superpowers – Google, Apple, Facebook, and Amazon. There are, however, many relevant instances of platforms that children and young people use. This includes platforms for socialising, platforms for audio-visual content, platforms that communicate with smart devices and toys, and platforms for games and sports franchises and platforms that provide services (including within in the public sector) that children or their parents use.

Young people choose to use platforms for play, socialising and expressing their identity. Adults have also introduced platforms into children’s lives: for example Capita SIMS is a platform used by over 80% of schools in the UK for assessment and monitoring (over the coming months at the Oxford Internet Institute we will be studying such platforms, including SIMS, for The Oak Foundation). Platforms for personal use have been facilitated by the popularity of tablets and smartphones.

Amongst the young, there has been a sharp uptake in tablet and smart phone usage at the expense of PC or laptop use. Sixteen per cent of 3-4 year olds have their own tablet, with this incidence doubling for 5-7 year olds. By the age of 12, smartphone ownership begins to outstrip tablet ownership (Ofcom, 2016). For our research at the OII, even when we included low-income families in our sample, 93% of teenagers owned a smartphone. This has brought forth the ‘appification’ of the web that Zittrain predicted in 2008. This means that children and young people predominately experience the internet via platforms that we can think of as controlled gateways to the open web.

Platforms exist to make money for investors

In public discourse some of these platforms are called social media. This term distracts us from the reason many of these publicly floated companies exist: to make money for their investors. It is only logical for all these companies to pursue the WeChat model that is becoming so popular in China. WeChat is a closed circuit platform, in that it keeps all engagements with the internet, including shopping, betting, and video calls, within its corporate compound. This brings WeChat closer to monopoly on data extraction.

Platforms have consolidated their success by buying out their competitors. Alphabet, Amazon, Apple, Facebook and Microsoft have made 436 acquisitions worth $131 billion over the last decade (Bloomberg, 2017). Alternatively, they just mimic the features of their competitors. For example, when Facebook acquired Instagram it introduced Stories, a feature use by Snapchat, which lets its users upload photos and videos as a ‘story’ (that automatically expires after 24 hours).

The more data these companies capture that their competitors are unable to capture, the more value they can extract from it and the better their business model works. It is unsurprising therefore that during our research we asked groups of teenagers to draw a visual representation of what they thought the world wide web and internet looked like – almost all of them just drew corporate logos (they also told us they had no idea that platforms such as Facebook own WhatsApp and Instagram, or that Google owns YouTube). Platform capitalism dominates and controls their digital experiences — but what provisions do these platforms make for children?

The General Data Protection Regulation (GDPR) (set to be implemented in all EU states, including the UK, in 2018) says that platforms collecting data about children below the age of 13 years shall only be lawful if and to the extent that consent is given or authorised by the child’s parent or custodian. Because most platforms are American-owned, they tend to apply a piece of Federal legislation known as COPPA; the age of consent for using Snapchat, WhatsApp, Facebook, and Twitter, for example, is therefore set at 13. Yet, the BBC found last year that 78% of children aged 10 to 12 had signed up to a platform, including Facebook, Instagram, Snapchat and WhatsApp.

Platform capitalism offloads its responsibilities onto the user

Why is this a problem? Firstly, because platform capitalism offloads any responsibility onto problematically normative constructs of childhood, parenting, and paternal relations. The owners of platforms assume children will always consult their parents before using their services and that parents will read and understand their terms and conditions, which, research confirms, in reality few users, children or adults, even look at.

Moreover, we found in our research many parents don’t have the knowledge, expertise, or time to monitor what their children are doing online. Some parents, for instance, worked night shifts or had more than one job. We talked to children who regularly moved between homes and whose estranged parents didn’t communicate with each other to supervise their children online. We found that parents who are in financial difficulties, or affected by mental and physical illness, are often unable to keep on top of their children’s digital lives.

We also interviewed children who use strategies to manage their parent’s anxieties so they would leave them alone. They would, for example, allow their parents to be their friends on Facebook, but do all their personal communication on other platforms that their parents knew nothing about. Often then the most vulnerable children offline, children in care for example, are the most vulnerable children online. My colleagues at the OII found 9 out of 10 of the teenagers who are bullied online also face regular ‘traditional’ bullying. Helping these children requires extra investment from their families, as well as teachers, charities and social services. The burden is on schools too to address the problem of fake news and extremism such as Holocaust denialism that children can read on platforms.

This is typical of platform capitalism. It monetises what are called social graphs: i.e. the networks of users who use its platforms that it then makes available to advertisers. Social graphs are more than just nodes and edges representing our social lives: they are embodiments of often intimate or very sensitive data (that can often be de-anonymised by linking, matching and combining digital profiles). When graphs become dysfunctional and manifest social problems such as abuse, doxxing, stalking, and grooming), local social systems and institutions — that are usually publicly funded — have to deal with the fall-out. These institutions are often either under-resourced and ill-equipped to these solve such problems, or they are already overburdened.

Are platforms too powerful?

The second problem is the ecosystems of dependency that emerge, within which smaller companies or other corporations try to monetise their associations with successful platforms: they seek to get in on the monopolies of data extraction that the big platforms are creating. Many of these companies are not wealthy corporations and therefore don’t have the infrastructure or expertise to develop their own robust security measures. They can cut costs by neglecting security or they subcontract out services to yet more companies that are added to the network of data sharers.

Again, the platforms offload any responsibility onto the user. For example, WhatsApp tells its users; “Please note that when you use third-party services, their own terms and privacy policies will govern your use of those services”. These ecosystems are networks that are only as strong as their weakest link. There are many infamous examples that illustrate this, including the so-called ‘Snappening’ where sexually explicit pictures harvested from Snapchat — a platform that is popular with teenagers — were released on to the open web. There is also a growing industry in fake apps that enable illegal data capture and fraud by leveraging the implicit trust users have for corporate walled gardens.

What can we do about these problems? Platform capitalism is restructuring labour markets and social relations in such a way that opting out from it is becoming an option available only to a privileged few. Moreover, we found teenagers whose parents prohibited them from using social platforms often felt socially isolated and stigmatised. In the real world of messy social reality, platforms can’t continue to offload their responsibilities on parents and schools.

We need some solutions fast because, by tacitly accepting the terms and conditions of platform capitalism – particularly when that they tell us it is not responsible for the harms its business model can facilitate – we may now be passing an event horizon where these companies are becoming too powerful, unaccountable, and distant from our local reality.

References

Hugh Cunningham (2009) Children and Childhood in Western Society Since 1500. Routledge.

Sonia Livingstone, Amanda Third (2017) Children and young people’s rights in the digital age: An emerging agenda. New Media and Society 19 (5).

Deborah Lupton, Ben Williamson (2017) The datafied child: The dataveillance of children and implications for their rights. New Media and Society 19 (5).

Nick Srnicek (2016) Platform Capitalism. Wiley.

The workshop welcomed a wide range of advocates in areas of intimate partner violence and sex work; engineers, designers, developers, and academics working on IT ethics. The objectives of the day were threefold:

(1) to better understand the lack of gender considerations in technology design,

(2) to formulate critical questions for functional requirement discussions between advocates and developers of gender-based violence applications; and

(3) to establish a set of criteria by which new applications can be assessed from a gender perspective.

Following three conceptual takeaways from the workshop, we share instructive primers for developers interested in creating technologies for those affected by gender-based violence.

Survivors, sex workers, and young people are intentional technology users

Increasing public awareness of the prevalence gender-based violence, both on and offline, often frames survivors of gender-based violence, activists, and young people as vulnerable and helpless. Contrary to this representation, those affected by gender-based violence are intentional technology users, choosing to adopt or abandon tools as they see fit. For example, sexual assault victims strategically disclose their stories on specific social media platforms to mobilize collective action. Sex workers adopt locative technologies to make safety plans. Young people utilize secure search tools to find information about sexual health resources near them. To fully understand how and why some technologies appear to do more for these communities, developers need to pay greater attention to the depth of their lived experience with technology.

Context matters

Technologies designed with good intentions do not inherently achieve their stated objectives. Functions that we take for granted to be neutral, such as a ‘Find my iPhone’ feature, can have unintended consequences. In contexts of gender-based violence, abusers and survivors appropriate these technological tools. For example, survivors and sex workers can use such a feature to share their whereabouts with friends in times of need. Abusers, on the other hand, can use the locative functions to stalk their victims. It is crucial to consider the context within which a technology is used, the user’s relationship to their environment, their needs, and interests so that technologies can begin to support those affected by gender-based violence.

Vulnerable communities perceive unique affordances

Drawing from ecological psychology, technology scholars have described this tension between design and use as affordance, to explain how a user’s perception of what can and cannot be done on a device informs their use. Designers may create a technology with a specific use in mind, but users will appropriate, resist, and improvise their use of the features as they see fit. For example, the use of a hashtags like #SurvivorPrivilege is an example of how rape victims create in-groups on Twitter to engage in supportive discussions, without the intention of it going viral.

Action Item

1. Predict unintended outcomes

Relatedly, the idea of devices as having affordances allows us to detect how technologies lead to unintended outcomes. Facebook’s ‘authentic name’ policy may have been instituted to promote safety for victims of relationship violence. The social and political contexts in which this policy is used, however, disproportionately affects the safety of human rights activists, drag queens, sex workers, and others — including survivors of partner violence.

2. Question the default

Technology developers are in a position to design the default settings of their technology. Since such settings are typically left unchanged by users, developers must take into account the effect on their target end users. For example, the default notification setting for text messages display the full message content in home screen. A smartphone user may experience texting as a private activity, but the default setting enables other people who are physically co-present to be involved. Opting out of this default setting requires some technical knowledge from the user. In abusive relationships, the abuser can therefore easily access the victim’s text messages through this default setting. So, in designing smartphone applications for survivors, developers should question the default privacy setting.

3. Inclusivity is not generalizability

There appears to be an equation of generalizability with inclusivity. An alarm button that claims to be for generally safety purposes may take a one-size-fits-all approach by automatically connecting the user to law enforcement. In cases of sexual assault, especially involving those who are of color, in sex work, or of LGBTQ identities, survivors are likely to avoid such features precisely because of its connection to law enforcement. This means that those who are most vulnerable are inadvertently excluded from the feature. Alternatively, an alarm feature that centers on these communities may direct the user to local resources. Thus, a feature that is generalizable may overlook target groups it aims to support; a more targeted feature may have less reach, but meet its objective. Just as communities’ needs are context-based, inclusivity, too, is contextualized. Developers should realize that that the broader mission of inclusivity can in fact be completed by addressing a specific need, though this may reduce the scope of end-users.

4. Consider co-designing

How, then, can we develop targeted technologies? Workshop participants suggested co-design (similarly, user-participatory design) as a process through which marginalized communities can take a leading role in developing new technologies. Instead of thinking about communities as passive recipients of technological tools, co-design positions both target communities and technologists as active agents who share skills and knowledge to develop innovative, technological interventions.

5. Involve funders and donors

Breakout group discussions pointed out how developers’ organizational and funding structures play a key role in shaping the kind of technologies they create. Suggested strategies included (1) educating donors about the specific social issue being addressed, (2) carefully considering whether funding sources meet developers’ objectives, and (3) ensuring diversity in the development team.

6. Do no harm with your research

In conducting user research, academics and technologists aim to better understand marginalized groups’ technology uses because they are typically at the forefront of adopting and appropriating digital tools. While it is important to expand our understanding of vulnerable communities’ everyday experience with technology, research on this topic can be used by authorities to further marginalize and target these communities. Take, for example, how tech startups like this align with law enforcement in ways that negatively affect sex workers. To ensure that research done about communities can actually contribute to supporting those communities, academics and developers must be vigilant and cautious about conducting ethical research that protects its subjects.

7. Should this app exist?

The most important question to address at the beginning of a technology design process should be: Should there even be an app for this? The idea that technologies can solve social problems as long as the technologists just “nerd harder” continues to guide the development and funding of new technologies. Many social problems are not necessarily data problems that can be solved by an efficient design and padded with enhanced privacy features. One necessary early strategy of intervention is to simply raise the question of whether technologies truly have a place in the particular context and, if so, whether it addresses a specific need.

Our workshop began with big questions about the intersections of gender-based violence and technology, and concluded with a simple but piercing question: Who designs what for whom? Implicated here are the complex workings of gender, sexuality, and power embedded in the lifetime of newly emerging devices from design to use. Apps and platforms can certainly have their place when confronting social problems, but the flow of data and the revealed information must be carefully tailored to the target context.

If you want to be involved with these future projects, please contact Kate Sim or Ben Zevenbergen.

The workshop was funded by the Princeton’s Center for Information Technology Policy (CITP), Princeton’s University Center for Human Values, the Ford Foundation, the Mozilla Foundation, and Princeton’s Council on Science and Technology.

This post was originally posted on CITP’s Freedom to Tinker blog.

While nearly a third of the adolescent respondents reported experiencing significant bullying in the past few months, cyberbullying was much less common, with around five percent of respondents reporting recent significant experiences. Both traditional and cyberbullying were independently associated with lower mental well-being, but only the relation between traditional bullying and well-being was robust. This supports the view that cyberbullying is unlikely to provide a source for new victims, but rather presents an avenue for further victimisation of those already suffering from traditional forms of bullying.

This stands in stark contrast to media reports and the popular perception that young people are now more likely to be victims of cyberbullying than traditional forms. The results also suggest that interventions to address cyberbullying will only be effective if they also consider the dynamics of traditional forms of bullying, supporting the urgent need for evidence-based interventions that target *both* forms of bullying in adolescence. That said, as social media and Internet connectivity become an increasingly intrinsic part of modern childhood, initiatives fostering resilience in online and every day contexts will be required.

We caught up with Andy and Lucy to discuss their findings:

Ed.: You say that given “the rise in the use of mobile and online technologies among young people, an up to date estimation of the current prevalence of cyberbullying in the UK is needed.” Having undertaken that—what are your initial thoughts on the results?

Andy: I think a really compelling thing we learned in this project is that researchers and policymakers have to think very carefully about what constitutes a meaningful degree of bullying or cyberbullying. Many of the studies and reports we reviewed were really loose on details here while a smaller core of work was precise and informative. When we started our study it was difficult to sort through the noise but we settled on a solid standard—at least two or three experiences of bullying in the past month—to base our prevalence numbers and statistical models on.

Lucy: One of the issues here is that studies often use different measures, so it is hard to compare like for like, but in general our study supports other recent studies indicating that relatively few adolescents report being cyberbullied only—one study by Dieter Wolke and colleagues that collected between 2014-2015 found that whilst 29% of school students reported being bullied, only 1% of 11-16 year olds reported only cyberbullying. Whilst that study was only in a handful of schools in one part of England, the findings are strikingly similar to our own. In general then it seems that rates of cyberbullying are not increasing dramatically; though it is concerning that prevalence rates of both forms of bullying—particularly traditional bullying—have remained unacceptably high.

Ed.: Is there a policy distinction drawn between “bullying” (i.e. young people) and “harassment” (i.e. the rest of us, including in the workplace)—and also between “bullying” and “cyber-bullying”? These are all basically the same thing, aren’t they—why distinguish?

Lucy: I think this is a good point; people do refer to ‘bullying’ in the workplace as well. Bullying, at its core, is defined as intentional, repeated aggression targeted against a person who is less able to defend him or herself—for example, a younger or more vulnerable person. Cyberbullying has the additional definition of occurring only in an online format—but I agree that this is the same action or behaviour, just taking place in a different context. Whilst in practice bullying and harassment have very similar meanings and may be used interchangeably, harassment is unlawful under the Equality Act 2010, whilst bullying actually isn’t a legal term at all. However certain acts of bullying could be considered harassment and therefore be prosecuted. I think this really just reflects the fact that we often ‘carve up’ human behaviour and experience according to our different policies, practices and research fields—when in reality they are not so distinct.

Ed.: I suppose online bullying of young people might be more difficult to deal with, given it can occur under the radar, and in social spaces that might not easily admit adults (though conversely, leave actual evidence, if reported..). Why do you think there’s a moral panic about cyberbullying — is it just newspapers selling copy, or does it say something interesting about the Internet as a medium — a space that’s both very open and very closed? And does any of this hysteria affect actual policy?

Andy: I think our concern arises from the uncertainty and unfamiliarity people have about the possibilities the Internet provides. Because it is full of potential—for good and ill—and is always changing, wild claims about it capture our imagination and fears. That said, the panic absolutely does affect policy and parenting discussions in the UK. Statistics and figures coming from pressure groups and well-meaning charities do put the prevalence of cyberbullying at terrifying, and unrealistically high, levels. This certainly has affected the way parents see things. Policy makers tend to seize on the worse case scenario and interpret things through this lens. Unfortunately this can be a distraction when there are known health and behavioural challenges facing young people.

Lucy: For me, I think we do tend to panic and highlight the negative impacts of the online world—often at the expense of the many positive impacts. That said, there was—and remains—a worry that cyberbullying could have the potential to be more widespread, and to be more difficult to resolve. The perpetrator’s identity may be unknown, may follow the child home from school, and may be persistent—in that it may be difficult to remove hurtful comments or photos from the Internet. It is reassuring that our findings, as well as others’, suggest that cyberbullying may not be associated with as great an impact on well-being as people have suggested.

Ed.: Obviously something as deeply complex and social as bullying requires a complex, multivalent response: but (that said), do you think there are any low-hanging interventions that might help address online bullying, like age verification, reporting tools, more information in online spaces about available help, more discussion of it as a problem (etc.)?

Andy: No easy ones. Understanding that cyber- and traditional bullying aren’t dissimilar, parental engagement and keeping lines of communication open are key. This means parents should learn about the technology their young people are using, and that kids should know they’re safe disclosing when something scary or distressing eventually happens.

Lucy: Bullying is certainly complex; school-based interventions that have been successful in reducing more traditional forms of bullying have tended to involve those students who are not directly involved but who act as ‘bystanders’—encouraging them to take a more active stance against bullying rather than remaining silent and implicitly suggesting that it is acceptable. There are online equivalents being developed, and greater education that discourages people (both children and adults) from sharing negative images or words, or encourages them to actively ‘dislike’ such negative posts show promise. I also think it’s important that targeted advice and support for those directly affected is provided.

Ed.: Who’s seen as the primary body responsible for dealing with bullying online: is it schools? NGOs? Or the platform owners who actually (if not-intentionally) host this abuse? And does this topic bump up against wider current concerns about (e.g.) the moral responsibilities of social media companies?

Andy: There is no single body that takes responsibility for this for young people. Some charities and government agencies, like the Child Exploitation and Online Protection command (CEOP) are doing great work. They provide a forum for information for parents and professionals for kids that is stratified by age, and easy-to-complete forms that young people or carers can use to get help. Most industry-based solutions require users to report and flag offensive content and they’re pretty far behind the ball on this because we don’t know what works and what doesn’t. At present cyberbullying consultants occupy the space and the services they provide are of dubious empirical value. If industry and the government want to improve things on this front they need to make direct investments in supporting robust, open, basic scientific research into cyberbulling and trials of promising intervention approaches.

Lucy: There was an interesting discussion by the NSPCC about this recently, and it seems that people are very mixed in their opinions—some would also say parents play an important role, as well as Government. I think this reflects the fact that cyberbullying is a complex social issue. It is important that social media companies are aware, and work with government, NGOs and young people to safeguard against harm (as many are doing), but equally schools and parents play an important role in educating children about cyberbullying—how to stay safe, how to play an active role in reducing cyberbullying, and who to turn to if children are experiencing cyberbullying.

Ed.: You mention various limitations to the study; what further evidence do you think we need, in order to more completely understand this issue, and support good interventions?

Lucy: I think we need to know more about how to support children directly affected by bullying, and more work is needed in developing effective interventions for cyberbullying. There are some very good school-based interventions with a strong evidence base to suggest that they reduce the prevalence of at least traditional forms of bullying, but they are not being widely implemented in the UK, and this is a missed opportunity.

Andy: I agree—a focus on flashy cyberbullying headlines presents the real risk of distracting us from developing and implementing evidence-based interventions. The Internet cannot be turned off and there are no simple solutions.

Ed.: You say the UK is ranked 20th of 27 EU countries on the mental well-being index, and also note the link between well-being and productivity. Do you think there’s enough discussion and effort being put into well-being, generally? And is there even a general public understanding of what “well-being” encompasses?

Lucy: I think the public understanding of well-being is probably pretty close to the research definition—people have a good sense that this involves more than not having psychological difficulty for example, and that it refers to friendships, relationships, and doing well; one’s overall quality of life. Both research and policy is placing more of an emphasis on well-being—in part because large international studies have suggested that the UK may score particularly poorly on measures of well-being. This is very important if we are going to raise standards and improve people’s quality of life.

Read the full article: Andrew Przybylski and Lucy Bowes (2017) Cyberbullying and adolescent well-being in England: a population-based cross sectional study. The Lancet Child & Adolescent Health.

Andrew Przybylski is an experimental psychologist based at the Oxford Internet Institute. His research focuses on applying motivational theory to understand the universal aspects of video games and social media that draw people in, the role of game structure and content on human aggression, and the factors that lead to successful versus unsuccessful self-regulation of gaming contexts and social media use. @ShuhBillSkee

Lucy Bowes is a Leverhulme Early Career Research Fellow at Oxford’s Department of Experimental Psychology. Her research focuses on the impact of early life stress on psychological and behavioural development, integrating social epidemiology, developmental psychology and behavioural genetics to understand the complex genetic and environmental influences that promote resilience to victimization and early life stress. @DrLucyBowes

Andy Przybylski and Lucy Bowes were talking to the Oxford Internet Institute’s Managing Editor, David Sutcliffe.

Letting your child use social media is like giving them cocaine, alcohol and cigarettes – all at once, or so we’re told. If you have been following recent press reports about the effects of social media on young people, you may well believe this. But there is no scientific evidence to support such extreme claims.

The real story is far more complex. It is very difficult to predict how social media will affect any specific individual – the effect depends on things like their personality, type of social media use and social surroundings. In reality, social media can have both positive and negative outcomes.

Media reports that compare social media to drug use are ignoring evidence of positive effects, while exaggerating and generalising the evidence of negative effects. This is scaremongering – and it does not promote healthy social media use. We would not liken giving children sweets to giving children drugs, even though having sweets for every meal could have serious health consequences. We should therefore not liken social media to drugs either.

For a claim to be proved scientifically it needs to be thoroughly tested. To fully confirm The Independent’s headline that: “Giving your child a smartphone is like giving them a gram of cocaine, says top addiction expert”, you would need to give children both a gram of cocaine and a smartphone and then compare the effects. Similarly, you would need to provide millennials with social media, drugs and alcohol to test The Conversation’s headline that: “Social media is as harmful as alcohol and drugs for millennials”. But ethical guidelines at universities were put in place so that such studies will never be done.

The diversity of social media

But maybe news headlines should be discounted – as exaggerations are often used to grab the readers’ attention. But even when ignoring these grand claims, the media coverage of social media is still misleading. For example, reports that talk about the effects of social media are often oversimplifying reality. Social media is incredibly diverse – different sites providing a host of different features. This makes it extremely difficult to generalise about social media’s effects.

A recent review of past research concluded that the effect of Facebook depends on which of the platform’s features you use. A dialog with friends over Facebook messenger can improve your mood, while comparing your life to other people’s photos on the Newsfeed can do the opposite. By treating all social media sites and features as one concept, the media is oversimplifying something that is very complex.

Focusing on the negative

Past media coverage has not only oversimplified social media, but has often only focused on social media’s negative aspects. But scientific research demonstrates that there are both positive and negative outcomes of social media use. Research has shown that Facebook increases self-esteem and promotes feeling connected to others. People’s physiological reactions also indicate they react positively to Facebook use.

By contrast, it has also been found that social media can decrease well-being and increases social anxiety. An analysis of 57 scientific studies found that social media is associated with slightly higher levels of narcissism. This array of conflicting evidence suggests that social media has both negative and positive effects. Not just one or the other.

The amount matters

The effect of social media also depends on the amount of time you spend using it. In a recent study we conducted of more than 120,000 UK teenagers, we found that moderate social media use is not harmful to mental health.

We compared the relationship between screen time and well-being. We found that those who used screens a moderate amount – between one and three hours each day – reported higher well-being compared with those who didn’t use social media at all and those who used it more than three hours a day. So, unlike drugs, those who practise abstinence do not appear to fare better.

Recent media reports may have made parents unnecessarily anxious about their child’s use of social media. A flashy quote or headline can often distract from the real challenges of parenting. It’s time the media covered not only the bad, but also the beneficial and complex sides of social media. The effects of social media cannot be summarised by comparing social media to drugs. It is just not that simple.

Andy Przybylski, Associate Professor and Senior Research Fellow, University of Oxford and Amy C Orben, College Lecturer and DPhil Candidate, University of Oxford

This article was originally published on The Conversation. Read the original article.

Three Trends

1. The proliferation of Wi-Fi means when children can use smart phones or tablets in variety of new contexts including on buses and trains, in hotels and restaurants, in school, libraries and health centre waiting rooms.

2. Research confirms apps on smart phones and tablets are now children’s primary gateway to the Web. This is the appification of the Web that Jonathon Zittrain predicted: the WeChat app, popular in China, is becoming its full realisation.

3. Simultaneously, the rapid expansion of the Internet of Things means everything is becoming ‘smart’ – phones, cars, toys, baby monitors, watches, toasters: we are even promised smart cities. Essentially, this means these devices have an IP address that allows to them receive, process, and transmit data on the Internet. Often these devices (including personal assistants like Alexa, game consoles and smart TVs) are picking up data produced by children. Marketing about smart toys tells us they are enhancing children’s play, augmenting children’s learning, incentivising children’s healthy habits and can even reclaim family time. Salient examples include Hello Barbie and Smart Toy Bear, which use voice and/or image recognition and connect to the cloud to analyse, process, and respond to children’s conversations and images. This sector is expanding to include app-enabled toys such as toy drones, cars, and droids (e.g. Star Wars BB-8); toys-to-life, which connect action figures to video games (e.g. Skylanders, Amiibo); puzzle and building games (e.g. Osmo, Lego Fusion); and children’s GPS-enabled wearables such as smart watches and fitness trackers. We need to look beyond the marketing to see what is making this technology ubiquitous.

The commercial incentives to collect children’s data

Service providers now use free Wi-Fi as an additional enticement to their customers, including families. Apps offer companies opportunities to contain children’s usage in a walled-garden so that they can capture valuable marketing data, or offer children and parents opportunities to make in-app purchases. Therefore, more and more companies, especially companies that have no background in technology such as bus operators and cereal manufactures, use Wi-Fi and apps to engage with children.

The smart label is also a new way for companies to differentiate their products from others in saturated markets that overwhelm consumers with choice. However, security is an additional cost that manufactures of smart technologies manufacturers are unwilling to pay. The microprocessors in smart toys often don’t have the processing power required for strong security measures and secure communication, such as encryption (e.g. an 8-bit microcontroller cannot support the industry standard SSL to encrypt communications). Therefore these devices are designed without the ability to accommodate software or firmware updates. Some smart toys transmit data in clear text (parents of course are unaware of such details when purchasing these toys).

While children are using their devices they are constantly emitting data. Because this data is so valuable to businesses it has become a cliché to frame it as an exploitable ‘natural’ resource like oil. This means every digitisable movement, transaction and interaction we make is potentially commodifiable. Moreover, the networks of specialist companies, partners and affiliates that capture, store process, broker and resell the new oil are becoming so complex they are impenetrable. This includes the involvement of commercial actors in public institutions such as schools.

Lupton & Williamson (2017) use the term ‘datafied child’ to draw attention to this creeping normalisation of harvesting data about children. As its provenance becomes more opaque the data is orphaned and vulnerable to further commodification. And when it is shared across unencrypted channels or stored using weak security (as high profile cases show) it is easily hacked. The implications of this are only beginning to emerge. In response, children’s rights, privacy and protection; the particular ethics of the capture and management of children’s data; and its potential for commercial exploitation are all beginning to receive more attention.

Refocusing on children

Apart from a ticked box, companies have no way of knowing if a parent or child has given their consent. Children, or their parents, will often sign away their data to quickly dispatch any impediment to accessing the Wi-Fi. When children use public Wi-Fi they are opening, often unencrypted, channels to their devices. We need to start mapping the range of actors who are collecting data in this way and find out if they have any provisions for protecting children’s data.

Similarly, when children use their apps, companies assume that a responsible adult has agreed to the terms and conditions. Parents are expected to be gatekeepers, boundary setters, and supervisors. However, for various reasons, there may not be an informed, (digitally) literate adult on hand. For example, parents may be too busy with work or too ill to stay on top of their children’s complex digital lives. Children are educated in year groups but they share digital networks and practices with older children and teenagers, including siblings, extended family members, and friends who may enable risky practices.

We may need to start looking at additional ways of protecting children that transfers the burden away from the family and to companies that are capturing and monetising the data. This includes being realistic about the efficacy of current legislation. Because children can simply enter a fake birthdate, application of the US Children’s Online Privacy Protection Act to restrict the collection of children’s personal data online has been fairly ineffectual (boyd et al., 2011). In Europe, the incoming General Data Protection Regulation allows EU states to set a minimum age of 16 under which children cannot consent to having their data processed, potentially encouraging and even larger population of minors to lie about their age online.

We need to ask what would data capture and management look like if it is guided by a children’s framework such as this one developed here by Sonia Livingstone and endorsed by the Children’s Commissioner here. Perhaps only companies that complied with strong security and anonymisation procedures would be licenced to trade in UK? Given the financial drivers at work, an ideal solution would possibly make better regulation a commerical incentive. We will be exploring these and other similar questions that emerge over the coming months.

This work is part of the OII project “Child safety on the Internet: looking beyond ICT actors“, which maps the range of non-ICT companies engaging digitally with children and identifying areas where their actions might affect a child’s exposure to online risks such as data theft, adverse online experiences or sexual exploitation. It is funded by the Oak Foundation.

In many sectors, automation is seen as a threat due to the potential for job losses. By contrast, automation is seen as an opportunity in healthcare, as a way to address pressures including staff shortages, increasing demand and workloads, reduced budget, skills shortages, and decreased consultation times. Automation may address these pressures in primary care, while also reconfiguring the work of staff roles and changing the patient-doctor relationship.

In the interview below, Matt Willis discusses a project, funded by The Health Foundation, which looks at opportunities and challenges to automation in NHS England general practice services. While the main goal of the project is to classify work tasks and then calculate the probability that each task will be automated, Matt is currently conducting ethnographic fieldwork in primary care sites to understand the work practices of surgery staff and clinicians.

Since the first automated pill counting machine was introduced in 1970 the role of the pharmacist has expanded to where they now perform more patient consultations, consult with primary care physicians, and require greater technical skill (including a Pharm.D degree). While this provides one clear way in which a medical profession has responded to automation, the research team is now looking at how automation will reconfigure other professions in primary care, and how it will shape its technical and digital infrastructures.

We caught up with Matt Willis to explore the implications of automation in primary care.

Ed.: One finding from an analysis by Frey and Osborne is that most healthcare occupations (that involve things like social intelligence, caring etc.) show a remarkably low probability for computerisation. But what sorts of things could be automated, despite that?

Matt: While providing care is the most important work that happens in primary care, there are many tasks that support that care. Many of those tasks are highly structured and repetitive, ideal things we can automate. There is an incredible amount of what I call “letter work” that occurs in primary care. It’s tasks like responding to requests for information from secondary care, an information request from a medical supplier, processing a trusted assessment, and so on.

There is also generating the letters that are sent to other parts of the NHS — and letters are also triaged at the beginning of each day depending on the urgency of the request. Medical coding is another task that can be automated as well as medication orders and renewal. All of these tasks require someone working with paper or digital text documents and gathering information according to a set of criteria. Often surgeries are overwhelmed with paperwork, so automation is a potential way to make a dent in the way information is processed.

Ed.: I suppose that the increasing digitisation of sensors and data capture (e.g. digital thermometers) and patient records actually helps in this: i.e. automation sounds like the obvious next step in an increasingly digital environment? But is it really as simple as that?

Matt: Well, it’s never as simple as you think it’s going to be. The commonality of data originating in a digital format usually does make data easier to work with, manipulate, analyze, and make actionable. Even when information is entirely digital there can be barriers of interoperability between systems. Automation could even be automating the use of data from one system to the next. There are also social and policy barriers to the use of digital data for automation. Think back to the recent care.data debacle that was supposed to centralize much of the NHS data from disparate silos.

Ed.: So will automation of these tasks be driven by government / within the NHS, or by industry / the market? i.e. is there already a market for automating aspects of healthcare?

Matt: Oh yes, I think it will be a variety of those forces you mention. There is already partial automation in many little ways all over NHS. Automation of messages and notifications, blood pressure cuffs, and other medical devices. Automation is not entirely new to healthcare. The pharmacist is an exemplar health profession to look at if we want to see how automation has changed the tasks of a profession for decades. Many of the electronic health record providers in the UK have different workflow automation features or let clinicians develop workflow efficiency protocols that may automate things in specific ways.

Ed.: You say that one of the bottlenecks to automating healthcare is lack of detailed knowledge of the sorts of tasks that could actually be automated. Is this what you’re working on now?

Matt: Absolutely. The data from labour statistics is self-reported and many of the occupations were lumped together meaning all receptionists in different sectors are just listed under receptionist. One early finding I have that I have been thinking about is how a receptionist in the healthcare sector is different in their information work than a receptionist’s counterpart in another sector. I see this with occupations across health, that there are unique features that differentiate health occupations from similar occupations. This begs the need to tease out those details in the data.

Additionally, we need to understand the use of technologies in primary care and what tasks those technologies perform. One of the most important links I am trying to understand is that between the tasks of people and the tasks of technologies. I am working on not only understanding the opportunities and challenges of automation in primary care but also what are the precursors that exist that may support the implementation of automation.

Ed.: When I started in journals publishing I went to the post room every day to mail out hardcopy proofs to authors. Now everything I do is electronic. I’m not really aware of when the shift happened, or what I do with the time freed up (blog, I suppose..). Do you think it will be similarly difficult in healthcare to pin-point a moment when “things got automated”?

Matt: Well, often times with technology and the change of social practices it’s rarely something that happens overnight. You probably started to gradually send out less and less paper manuscripts over a period of time. It’s the frog sitting in a pot where the heat is slowly turned up. There is a theory that technological change comes in swarm patterns — meaning it’s not one technological change that upends everything, but the advent of numerous technologies that start to create big change.

For example, one of the many reasons that the application of automation technologies is increasing is the swarming of prior technologies like “big data” sets, advances in machine vision, machine learning, machine pattern recognition, mobile robotics, the proliferation of sensors, and further development of autonomous technologies. These kinds of things drive big advances forward.

Ed.: I don’t know if people in the publishing house I worked in lost their jobs when things like post rooms and tea trolleys got replaced by email and coffee machines — or were simply moved to different types of jobs. Do you think people will “lose their jobs“ as automation spreads through the health sector, or will it just drive a shift to people doing something else instead?

Matt: One of the justifications in the project is that in many sectors automation is seen as a threat, however, automation is seen as an opportunity in healthcare. This is in great part due to the current state of the NHS and that the smart and appropriate application of automation technologies can be a force multiplier, particularly in primary care.

I see it as not that people will be put out of jobs, but that you’ll be less likely to have to work 12 hours when you should be working 8 and to not have a pile of documents stacking up that you are three months behind in processing. The demand for healthcare is increasing, the population is aging, and people live longer. One of the ways to keep up with this trend is to implement automation technologies that support healthcare workers and management.

I think we are a long ways away from the science fiction future where a patient lays in an entirely automated medical pod that scans them and administers whatever drug, treatment, procedure, or surgery they need. A person’s tasks and the allocation of work will shift in part due to technology. But that has been happening for decades. There is also a longstanding debate about if technology creates more jobs in the long term than it destroys. It’s likely that in healthcare we will see new occupational roles, job titles, and tasks emerge that are in part automation related. Also, that tasks like filing paperwork or writing a letter will seem barbaric when a computer can, through little time and effort, do that for you.

Matthew Willis was talking to blog editor David Sutcliffe.

“Self-Tracking” (MIT Press) by Gina Neff and Dawn Nafus examines how people record, analyze, and reflect on this data — looking at the tools they use and the communities they become part of, and offering an introduction to the essential ideas and key challenges of using these technologies. In considering self-tracking as a social and cultural phenomenon, they describe not only the use of data as a kind of mirror of the self but also how this enables people to connect to, and learn from, others.

They also consider what’s at stake: who wants our data and why, the practices of serious self-tracking enthusiasts, the design of commercial self-tracking technology, and how people are turning to self-tracking to fill gaps in the healthcare system. None of us can lead an entirely untracked life today, but in their book, Gina and Dawn show us how to use our data in a way that empowers and educates us.

We caught up with Gina to explore the self-tracking movement:

Ed.: Over one hundred million wearable sensors were shipped last year to help us gather data about our lives. Is the trend and market for personal health-monitoring devices ever-increasing, or are we seeing saturation of the device market and the things people might conceivably want to (pay to) monitor about themselves?

Gina: By focusing on direct-to-consumer wearables and mobile apps for health and wellness in the US we see a lot of tech developed with very little focus on impact or efficacy. I think to some extent we’ve hit the trough in the ‘hype’ cycle, where the initial excitement over digital self-tracking is giving way to the hard and serious work of figuring out how to make things that improve people’s lives. Recent clinical trial data show that activity trackers, for example, don’t help people to lose weight. What we try to do in the book is to help people figure out what self-tracking to do for them and advocate for people being able to access and control their own data to help them ask — and answer — the questions that they have.

Ed.: A question I was too shy to ask the first time I saw you speak at the OII — how do you put the narrative back into the data? That is, how do you make stories that might mean something to a person, out of the vast piles of strangely meaningful-meaningless numbers that their devices accumulate about them?

Gina: We really emphasise community. It might sound clichéd but it truly helps. When I read some scholars’ critiques of the Quantified Self meetups that happen around the world I wonder if we have actually been to the same meetings. Instead of some kind of technophilia there are people really working to make sense of information about their lives. There’s a lot of love for tech, but there are also people trying to figure out what their numbers mean, are they normal, and how to design their own ‘n of 1’ trials to figure out how to make themselves better, healthier, and happier. Putting narrative back into data really involves sharing results with others and making sense together.

Ed.: There’s already been a lot of fuss about monetisation of NHS health records: I imagine the world of personal health / wellness data is a vast Wild West of opportunity for some (i.e. companies) and potential exploitation of others (i.e. the monitored), with little law or enforcement? For a start .. is this health data or social data? And are these equivalent forms of data, or are they afforded different protections?

Gina: In an opinion piece in Wired UK last summer I asked what happens to data ownership when your smartphone is your doctor. Right now we afford different privacy protection to health-related data than other forms of personal data. But very soon trace data may be useful for clinical diagnoses. There are already in place programmes for using trace data for early detection of mood disorders, and research is underway on using mobile data for the diagnosis of movement disorders. Who will have control and access to these potential early alert systems for our health information? Will it be legally protected to the same extent as the information in our medical records? These are questions that society needs to settle.

Ed.: I like the central irony of “mindfulness” (a meditation technique involving a deep awareness of your own body), i.e. that these devices reveal more about certain aspects of the state of your body than you would know yourself: but you have to focus on something outside of yourself (i.e. a device) to gain that knowledge. Do these monitoring devices support or defeat “mindfulness”?

Gina: I’m of two minds, no pun intended. Many of the Quantified Self experiments we discuss in the book involved people playing with their data in intentional ways and that level of reflection in turn influences how people connect the data about themselves to the changes they want to make in their behaviour. In other words, the act of self-tracking itself may help people to make changes. Some scholars have written about the ‘outsourcing’ of the self, while others have argued that we can develop ‘exosenses’ outside our bodies to extend our experience of the world, bringing us more haptic awareness. Personally, I do see the irony in smartphone apps intended to help us reconnect with ourselves.

Ed.: We are apparently willing to give up a huge amount of privacy (and monetizable data) for convenience, novelty, and to interact with seductive technologies. Is the main driving force of the wearable health-tech industry the actual devices themselves, or the data they collect? i.e. are these self-tracking companies primarily device/hardware companies or software/data companies?

Gina: Sadly, I think it is neither. The drop off in engagement with wearables and apps is steep with the majority falling into disuse after six months. Right now one of the primary concerns I have as an Internet scholar is the apparent lack of empathy companies seem to have for their customers in this space. People operate under the assumption that the data generated by the devices they purchase is ‘theirs’, yet companies too often operate as if they are the sole owners of that data.

Anthropologist Bill Maurer has proposed replacing data ownership with a notion of data ‘kinship’ – that both technology companies and their customers have rights and responsibilities to the data that they produce together. Until we have better social contracts and legal frameworks for people to have control and access to their own data in ways that allow them to extract it, query it, and combine it with other kinds of data, then that problem of engagement will continue and activity trackers will sit unused on bedside tables or uncharged in the back of drawers. The ability to help people ask the next question or design the next self-tracking experiment is where most wearables fail today.

Ed.: And is this data at all clinically useful / interoperable with healthcare and insurance systems? i.e. do the companies producing self-monitoring devices work to particular data and medical standards? And is there any auditing and certification of these devices, and the data they collect?

Gina: This idea that the data is just one interoperable system away from usefulness is seductive but so, so wrong. I was recently at a panel of health innovators, the title of which was ‘No more Apps’. The argument was that we’re not going to get to meaningful change in healthcare simply by adding a new data stream. Doctors in our study said things like ‘I don’t need more data; I need more resources.’ Right now we have few protections for individuals that this data won’t be able to harm their rights to insurance, or won’t be used to discriminate against them and yet there are few results that show how the commercially available wearable devices are delivering clinical value. There’s still a lot of work needed before this can happen.

Ed.: Lastly — just as we share our music on iTunes; could you see a scenario where we start to share our self-status with other device wearers? Maybe to increase our sociability and empathy by being able to send auto-congratulations to people who’ve walked a lot that day, or to show concern to people with elevated heart rates / skin conductivity (etc.)? Given the logical next step to accumulating things is to share them..

Gina: We can see that future scenario now in groups like Patients Like Me, Cure Together, and Quantified Self meetups. What these ‘edge’ use cases teach us for more everyday self-tracking uses is that real support and community can form around people sharing their data with others. These are projects that start from individuals with information about themselves and work to build toward collective, social knowledge. Other types of ‘citizen science’ projects are underway like the Personal Genome Project where people can donate their health data for science. The Stanford-led MyHeart Counts study on iPhone and Apple Watch recruited in its first two weeks 6,000 people for its study and now has over 40,000 US participants. Those are numbers for clinical studies that we’ve just never seen before.

My co-author led the development of an interesting tool, Data Sense, that lets people without stats training visualize the relationships among variables in their own data or easily combine their data with data from other people. When people can do that they can begin asking the questions that matter for them and for their communities. What we know won’t work in the future of self-tracking data, though, are the lightweight online communities that technology brands just throw together. I’m just not going to be motivated by a random message from LovesToWalk1949, but under the right conditions I might be motivated by my mom, my best friend or my social network. There is still a lot of hard work that has to be done to get the design of self-tracking tools, practices, and communities for social support right.

Gina Neff was talking to blog editor David Sutcliffe about her book (with Dawn Naffs) “Self-Tracking” (MIT Press).

While the UK government has financed technological infrastructure and invested in schemes to address digital inequalities, the outcomes of these schemes are rarely uniformly positive or transformative for the people involved. This gap between expectation and reality demands theoretical attention; with more attention placed on the cultural, political and economic contexts of the digitally excluded, and the various attempts to “include” them.

Focusing on a two-year digital inclusion scheme for 30 teenagers and their families initiated by a local council in England, a qualitative study by Huw C. Davies, Rebecca Eynon, and Sarah Wilkin analyses why, despite the good intentions of the scheme’s stakeholders, it fell short of its ambitions. It also explains how the neoliberalist systems of governance that are increasingly shaping the cultures and behaviours of Internet service providers and schools — that incentivise action that is counterproductive to addressing digital inequality and practices — cannot solve the problems they create.

We caught up with the authors to discuss the study’s findings:

Ed.: It was estimated that around 10% of 13 year olds in the study area lacked dependable access to the Internet, and had no laptop or PC at home. How does this impact educational outcomes?

Huw: It’s impossible to disaggregate technology from everything else that can affect a young person’s progress through school. However, one school in our study had transferred all its homework and assessments online while the other schools were progressing to this model. The students we worked with said doing research for homework is synonymous with using Google or Wikipedia, and it’s the norm to send homework and coursework to teachers by email, upload it to Virtual Learning Environments, or print it out at home. Therefore students who don’t have access to the Internet have to spend time and effort finding work-arounds such as using public libraries. Lack of access also excludes such students from casual learning from resources online or pursuing their own interests in their own time.

Ed.: The digital inclusion scheme was designed as a collaboration between a local council in England (who provided Internet services) and schools (who managed the scheme) in order to test the effect of providing home Internet access on educational outcomes in the area. What was your own involvement, as researchers?

Huw: Initially, we were the project’s expert consultants: we were there to offer advice, guidance and training to teachers and assess the project’s efficacy on its conclusion. However, as it progressed we took on the responsibility of providing skills training to the scheme’s students and technical support to their families. When it came to assessing the scheme, by interviewing young people and their families at their homes, we were therefore able to draw on our working knowledge of each family’s circumstances.

Ed.: What was the outcome of the digital inclusion project —- i.e. was it “successful”?

Huw: As we discuss in the article, defining success in these kinds of schemes is difficult. Subconsciously many people involved in these kinds of schemes expect technology to be transformative for the young people involved yet in reality the changes you see are more nuanced and subtle. Some of the scheme’s young people found apprenticeships or college courses, taught themselves new skills, used social networks for the first time and spoke to friends and relatives abroad by video for free. These success stories definitely made the scheme worthwhile. However, despite the significant good will of the schools, local council, and the families to make the scheme a success there were also frustrations and problems. In the article we talk about these problems and argue that the challenges the scheme encountered are not just practical issues to be resolved, but are systemic issues that need to be explicitly recognised in future schemes of this kind.

Ed.: And in the article you use neoliberalism as a frame to discuss these issues..?

Huw: Yes. But we recognise in the article that this is a concept that needs to be used with care. It’s often used pejoratively and/or imprecisely. We have taken it to mean a set of guiding principles that are intended to produce a better quality of services through competition, targets, results, incentives and penalties. The logic of these principles, we argue, influences they way organisations treat individual users of their services.

For example, for Internet Service Providers (ISPs) the logic of neoliberalism is to subcontract out the constituent parts of an overall service provision to create mini internal markets that (in theory) promote efficiency through competition. Yet this logic only really works if everyone comes to the market with similar resources and abilities to make choices. If their customers are well informed and wealthy enough to remind companies that they can take their business elsewhere these companies will have a strong incentive to improve their services and reduce their costs. If customers are disempowered by lack of choice the logic of neoliberalism tends to marginalise or ignore their needs. These were low-income families with little or no experience of exercising consumer choice and rights. For them therefore these mini markets didn’t work.

In the schools we worked with the logic of neoliberalism meant staff and students felt under pressure to meet certain targets — they all had to priortise things that were measured and measurable. Failure to meet these targets would then mean they would have to account for what went wrong, face losing out on a reward or they would expect disciplinary action. It therefore becomes much more difficult for schools to devote time and energy to schemes such as this.

Ed.: Were there any obvious lessons that might lead to a better outcome if the scheme were to be repeated: or are the (social, economic, political) problems just too intractable, and therefore too difficult and expensive to sort out?

Huw: Many of the families told us that access to the Internet was becoming evermore vital. This was not just for homework but also for access to public and health services (that are being increasingly delivered online) and getting to the best deals online for consumer services. They often told us therefore that they would do whatever it took to keep their connection after the two-year scheme ended. This often meant paying for broadband out of their social security benefits or income that was too low to be taxable: income that could otherwise have been spent on, for example, food and clothing. Given its necessity, we should have a national conversation about providing this service to low income families for free.

Ed.: Some of the families included in the study could be considered “hard to reach”. What were your experiences of working with them?

Huw: There are many practical and ethical issues to address before these sorts of schemes can begin. These families often face multiple intersecting problems that involve many agencies (who don’t necessarily communicate with each other) intervening in their lives. For example, some of the scheme’s families were dealing with mental illness, disability, poor housing, and debt all at the same time. It is important that such schemes are set up with an awareness of this complexity. We are very grateful to the families that took part in the scheme and the insights they gave us for how such schemes should run in the future.

Ed.: Finally, how do your findings inform all the studies showing that “digital inclusion schemes are rarely uniformly positive or transformative for the people involved”. Are these studies gradually leading to improved knowledge (and better policy intervention), or simply showing the extent of the problem without necessarily offering “solutions”?

Huw: We have tried to put this scheme into a broader context to show such policy interventions have to be much more ambitious, intelligent, and holistic. We never assumed digital inequality is an isolated problem that can be fixed with a free broadband connection, but when people are unable to afford the Internet it is an indication of other forms of disadvantage that, in a sympathetic and coordinated way, have to be addressed simultaneously. Hopefully, we have contributed to the growing awareness that such attempts to ameliorate the symptoms may offer some relief but should never be considered a cure in itself.

Read the full article: Huw C. Davies, Rebecca Eynon, Sarah Wilkin (2017) Neoliberal gremlins? How a scheme to help disadvantaged young people thrive online fell short of its ambitions. Information, Communication & Society. DOI: 10.1080/1369118X.2017.1293131

The article is an output of the project “Tackling Digital Inequality Amongst Young People: The Home Internet Access Initiative“, funded by Google.

Huw Davies was talking to blog editor David Sutcliffe.

A 2016 survey by OnePoll found that 40% of respondents felt they had “not truly experienced valuable moments such as a child’s first steps or graduation” because “technology got in the way”, and OfCom’s 2016 survey showed that 15 million British Internet users (representing a third of those online), have already tried a digital detox. In recent years, America has sought to pathologise a perceived over-use of digital technology as “Internet addiction”. While the term is not recognized by the DSM, the idea is commonly used in media rhetoric and forms an important backdrop to digital detoxing.

The article Disconnect to reconnect: The food/technology metaphor in digital detoxing (First Monday) by Theodora Sutton presents a short ethnography of the digital detoxing community in the San Francisco Bay Area. Her informants attend an annual four-day digital detox and summer camp for adults in the Californian forest called Camp Grounded. She attended two Camp Grounded sessions in 2014, and followed up with semi-structured interviews with eight detoxers.

We caught up with Theodora to examine the implications of the study and to learn more about her PhD research, which focuses on the same field site.

Ed.: In your forthcoming article you say that Camp Grounded attendees used food metaphors (and words like “snacking” and “nutrition”) to understand their own use of technology and behaviour. How useful is this as an analogy?

Theodora: The food/technology analogy is an incredibly neat way to talk about something we think of as immaterial in a more tangible way. We know that our digital world relies on physical connections, but we forget that all the time. Another thing it does in lending a dietary connotation is to imply we should regulate our consumption of digital use; that there are healthy and unhealthy or inappropriate ways of using it.

I explore more pros and cons to the analogy in the paper, but the biggest con in my opinion is that while it’s neat, it’s often used to make value judgments about technology use. For example, saying that online sociality is like processed food is implying that it lacks authenticity. So the food analogy is a really useful way to understand how people are interpreting technology culturally, but it’s important to be aware of how it’s used.

Ed.: How do people rationalise ideas of the digital being somehow “less real” or “genuine” (less “nourishing”), despite the fact that it obviously is all real: just different? Is it just a peg to blame an “other” and excuse their own behaviour .. rather than just switching off their phones and going for a run / sail etc. (or any other “real” activity..).

Theodora: The idea of new technologies being somehow less real or less natural is a pretty established Western concept, and it’s been fundamental in moral panics following new technologies. That digital sociality is different, not lesser, is something we can academically agree on, but people very often believe otherwise.

My personal view is that figuring out what kind of digital usage suits you and then acting in moderation is ideal, without the need for extreme lengths, but in reality moderation can be quite difficult to achieve. And the thing is, we’re not just talking about choosing to text rather than meet in person, or read a book instead of go on Twitter. We’re talking about digital activities that are increasingly inescapable and part of life, like work e-mail or government services being moved online.

The ability to go for a run or go sailing are again privileged activities for people with free time. Many people think getting back to nature or meeting in person are really important for human needs. But increasingly, not everyone has the ability to get away from devices, especially if you don’t have enough money to visit friends or travel to a forest, or you’re just too tired from working all the time. So Camp Grounded is part of what they feel is an urgent conversation about whether the technology we design addresses human, emotional needs.

Ed.: You write in the paper that “upon arrival at Camp Grounded, campers are met with hugs and milk and cookies” .. not to sound horrible, but isn’t this replacing one type of (self-focused) reassurance with another? I mean, it sounds really nice (as does the rest of the Camp), but it sounds a tiny bit like their “problem” is being fetishised / enjoyed a little bit? Or maybe that their problem isn’t to do with technology, but rather with confidence, anxiety etc.

Theodora: The people who run Camp Grounded would tell you themselves that digital detoxing is not really about digital technology. That’s just the current scapegoat for all the alienating aspects of modern life. They also take away real names, work talk, watches, and alcohol. One of the biggest things Camp Grounded tries to do is build up attendees’ confidence to be silly and playful and have their identities less tied to their work persona, which is a bit of a backlash against Silicon Valley’s intense work ethic. Milk and cookies comes from childhood, or America’s summer camps which many attendees went to as children, so it’s one little thing they do to get you to transition into that more relaxed and childlike way of behaving.

I’m not sure about “fetishized,” but Camp Grounded really jumps on board with the technology idea, using really ironic things like an analog dating service called “embers,” a “human powered search” where you pin questions on a big noticeboard and other people answer, and an “inbox” where people leave you letters.

And you’re right, there is an aspect of digital detoxing which is very much a “middle class ailment” in that it can seem rather surface-level and indulgent, and tickets are pretty pricey, making it quite a privileged activity. But at the same time I think it is a genuine conversation starter about our relationship with technology and how it’s designed. I think a digital detox is more than just escapism or reassurance, for them it’s about testing a different lifestyle, seeing what works best for them and learning from that.

Ed.: Many of these technologies are designed to be “addictive” (to use the term loosely: maybe I mean “seductive”) in order to drive engagement and encourage retention: is there maybe an analogy here with foods that are too sugary, salty, fatty (i.e. addictive) for us? I suppose the line between genuine addiction and free choice / agency is a difficult one; and one that may depend largely on the individual. Which presumably makes any attempts to regulate (or even just question) these persuasive digital environments particularly difficult? Given the massive outcry over perfectly rational attempts to tax sugar, fat etc.

Theodora: The analogy between sugary, salty, or fatty foods and seductive technologies is drawn a lot — it was even made by danah boyd in 2009. Digital detoxing comes from a standpoint that tech companies aren’t necessarily working to enable meaningful connection, and are instead aiming to “hook” people in. That’s often compared to food companies that exist to make a profit rather than improve your individual nutrition, using whatever salt, sugar, flavourings, or packaging they have at their disposal to make you keep coming back.

There are two different ways of “fixing” perceived problems with tech: there’s technical fixes that might only let you use the site for certain amounts of time, or re-designing it so that it’s less seductive; then there’s normative fixes, which could be on an individual level deciding to make a change, or even society wide, like the French labour law giving the “right to disconnect” from work emails on evenings and weekends.

One that sort of embodies both of these is The Time Well Spent project, run by Tristan Harris and the OII’s James Williams. They suggest different metrics for tech platforms, such as how well they enable good experiences away from the computer altogether. Like organic food stickers, they’ve suggested putting a stamp on websites whose companies have these different metrics. That could encourage people to demand better online experiences, and encourage tech companies to design accordingly.

So that’s one way that people are thinking about regulating it, but I think we’re still in the stages of sketching out what the actual problems are and thinking about how we can regulate or “fix” them. At the moment, the issue seems to depend on what the individual wants to do. I’d be really interested to know what other ideas people have had to regulate it, though.

Ed.: Without getting into the immense minefield of evolutionary psychology (and whether or not we are creating environments that might be detrimental to us mentally or socially: just as the Big Mac and Krispy Kreme are not brilliant for us nutritionally) — what is the lay of the land — the academic trends and camps — for this larger question of “Internet addiction” .. and whether or not it’s even a thing?

Theodora: In my experience academics don’t consider it a real thing, just as you wouldn’t say someone had an addiction to books. But again, that doesn’t mean it isn’t used all the time as a shorthand. And there are some academics who use it, like Kimberly Young who proposed it in the 1990’s. She still runs an Internet addiction treatment centre in New York, and there’s another in Fall City, Washington state.

The term certainly isn’t going away any time soon and the centres treat people who genuinely seem to have a very problematic relationship with their technology. People like the OII’s Andrew Przybylski (@ShuhBillSkee) are working on untangling this kind of problematic digital use from the idea of addiction, which can be a bit of a defeatist and dramatic term.

Ed.: As an ethnographer working at the Camp according to its rules (hand-written notes, analogue camera) .. did it affect your thinking or subsequent behaviour / habits in any way?

Theodora: Absolutely. In a way that’s a struggle, because I never felt that I wanted or needed a digital detox, yet having been to it three times now I can see the benefits. Going to camp made a strong case for the argument to be more careful with my technology use, for example not checking my phone mid-conversation, and I’ve been much more aware of it since. For me, that’s been part of an on-going debate that I have in my own life, which I think is a really useful fuel towards continuing to unravel this topic in my studies.

Ed.: So what are your plans now for your research in this area — will you be going back to Camp Grounded for another detox?

Theodora: Yes — I’ll be doing an ethnography of the digital detoxing community again this summer for my PhD and that will include attending Camp Grounded again. So far I’ve essentially done just preliminary fieldwork and visited to touch base with my informants. It’s easy to listen to the rhetoric around digital detoxing, but I think what’s been missing is someone spending time with them to really understand their point of view, especially their values, that you can’t always capture in a survey or in interviews.

In my PhD I hope to understand things like: how digital detoxers even think about technology, what kind of strategies they have to use it appropriately once they return from a detox, and how metaphor and language work in talking about the need to “unplug.” The food analogy is just one preliminary finding that shows how fascinating the topic is as soon as you start scratching away the surface.

Read the full article: Sutton, T. (2017) Disconnect to reconnect: The food/technology metaphor in digital detoxing. First Monday 22 (6).

OII DPhil student Theodora Sutton was talking to blog editor David Sutcliffe.

In the first attempt to estimate Internet use at any small-scale level, they combine data from a sample survey, the 2013 Oxford Internet Survey (OxIS), with the 2011 UK census, employing small area estimation to estimate Internet use in small geographies in Britain. (Read the paper for more on this method, and discussion of why there has been little work on the geography of digital inequality.)

There are two major reasons to suspect that geographic differences in Internet use may be important: apparent regional differences and the urban-rural divide. The authors do indeed find a regional difference: the area with least Internet use is in the North East, followed by central Wales; the highest is in London and the South East. But interestingly, geographic differences become non-significant after controlling for demographic variables (age, education, income etc.). That is, demographics matter more than simply where you live, in terms of the likelihood that you’re an Internet user.

Britain has one of the largest Internet economies in the developed world, and the Internet contributes an estimated 8.3 percent to Britain’s GDP. By reducing a range of geographic frictions and allowing access to new customers, markets and ideas it strongly supports domestic job and income growth. There are also personal benefits to Internet use. However, these advantages are denied to people who are not online, leading to a stream of research on the so-called digital divide.

We caught up with Grant Blank to discuss the policy implications of this marked disparity in (estimated) Internet use across Britain.

Ed.: The small-area estimation method you use combines the extreme breadth but shallowness of the national census, with the relative lack of breadth (2000 respondents) but extreme richness (550 variables) of the OxIS survey. Doing this allows you to estimate things like Internet use in fine-grained detail across all of Britain. Is this technique in standard use in government, to understand things like local demand for health services etc.? It seems pretty clever..

Grant: It is used by the government, but not extensively. It is complex and time-consuming to use well, and it requires considerable statistical skills. These have hampered its spread. It probably could be used more than it is — your example of local demand for health services is a good idea..

Ed.: You say this method works for Britain because OxIS collects information based on geographic area (rather than e.g. randomly by phone number) — so we can estimate things geographically for Britain that can’t be done for other countries in the World Internet Project (including the US, Canada, Sweden, Australia). What else will you be doing with the data, based on this happy fact?

Grant: We have used a straightforward measure of Internet use versus non-use as our dependent variable. Similar techniques could predict and map a variety of other variables. For example, we could take a more nuanced view of how people use the Internet. The patterns of mobile use versus fixed-line use may differ geographically and could be mapped. We could separate work-only users, teenagers using social media, or other subsets. Major Internet activities could be mapped, including such things as entertainment use, information gathering, commerce, and content production. In addition, the amount of use and the variety of uses could be mapped. All these are major issues and their geographic distribution has never been tracked.

Ed.: And what might you be able to do by integrating into this model another layer of geocoded (but perhaps not demographically rich or transparent) data, e.g. geolocated social media / Wikipedia activity (etc.)?

Grant: The strength of the data we have is that it is representative of the UK population. The other examples you mention, like Wikipedia activity or geolocated social media, are all done by smaller, self-selected groups of people, who are not at all representative. One possibility would be to show how and in what ways they are unrepresentative.

Ed.: If you say that Internet use actually correlates to the “usual” demographics, i.e. education, age, income — is there anything policy makers can realistically do with this information? i.e. other than hope that people go to school, never age, and get good jobs? What can policy-makers do with these findings?

Grant: The demographic characteristics are things that don’t change quickly. These results point to the limits of the government’s ability to move people online. They say that 100% of the UK population will never be online. This raises the question, what are realistic expectations for online activity? I don’t know the answer to that but it is an important question that is not easily addressed.

Ed.: You say that “The first law of the Internet is that everything is related to age”. When are we likely to have enough longitudinal data to understand whether this is simply because older people never had the chance to embed the Internet in their lives when they were younger, or whether it is indeed the case that older people inherently drop out. Will this age-effect eventually diminish or disappear?

Grant: You ask an important but unresolved question. In the language of social sciences — is the decline in Internet use with age an age-effect or a cohort-effect. An age-effect means that the Internet becomes less valuable as people age and so the decline in use with age is just a reflection of the declining value of the Internet. If this explanation is true then the age-effect will persist into the indefinite future. A cohort-effect implies that the reason older people tend to use the Internet less is that fewer of them learned to use the Internet in school or work. They will eventually be replaced by active Internet-using people and Internet use will no longer be associated with age. The decline with age will eventually disappear. We can address this question using data from the Oxford Internet Survey, but it is not a small area estimation problem.

Read the full article: Blank, G., Graham, M., and Calvino, C. 2017. Local Geographies of Digital Inequality. Social Science Computer Review. DOI: 10.1177/0894439317693332.

This work was supported by the Economic and Social Research Council [grant ES/K00283X/1]. The data have been deposited in the UK Data Archive under the name “Geography of Digital Inequality”.

Grant Blank was speaking to blog editor David Sutcliffe.

A research team at the OII has already been analysing the content, and provided cleaned data to the datahack participants that could be analysed through qualitative and quantitative methods. Following an introduction to the project by Laura Bates, an outline of the dataset by Taha Yasseri, and a speed-networking session led by Kathryn Eccles we fell into two teams to work with the data.

Our own group wanted to examine the question of how people interact with the threat of public space. We were also interested in how public space is divided between online and offline, and the social perception of being online versus offline. We wanted to explore what sorts of reactions people might have to examples of assault, or strategies or things they might do in response to something happening to them — and how they might differ online and offline.

We spent the first hour collecting keywords that might indicate reactions to either online or offline harassment, including identifying a perceived threat and coping with it. We then searched the raw data for responses like “I tried to ignore it” “I felt safe / unsafe” “I identified a risk” “I was feeling worried, feeling anxious or nervous“; and also looked at online versus offline actions. So for online action we were looking for specific platforms being named, and people saying things like “comment, response, delete, remove” in relation to social media posts. For offline we were looking for things like “I carried a [specific item]” or “I hid or avoided certain areas“ or “I walked faster” (etc.).

We wanted to know if we could apply ideas of responses to offline space back to online spaces, and how these online spaces fall short. Offline responses are often very individual, whereas you might not have such a direct and individual response to something like a Facebook ad. Taking into account the important caveat that this was just a quick exploration of the data — and that the data were indicative rather than representative (so should in no way be used to extrapolate or infer anything concrete) one of the biggest things we found was that while in the offline examples of responses to harassment there was quite a lot of action, like running away, or hiding in shops and restaurants, there were very few examples to responses in the online examples.

Though it actually turned out to be difficult to identify a clear division between online/offline contexts in the data: we saw accounts of people who were online on social media encountering something sexist and logging off, and then walking in the street and getting harassed. But it seemed like people were more likely to report something offline to the police than in online forums. And this contrast is very interesting, in terms of whether you can be an active agent in response to something, or whether there’s something about being online that positions you as being passive and unable to respond — and what we can do about that.

While we found it difficult to quantify, we did wonder if people might not be giving themselves credit for the kinds of responses they have to examples of sexism online — maybe they aren’t thinking about what they do. Whereas offline they might say “I ran away, because I was so scared” perhaps when it’s online, people just read it and not respond; or at least not report responses to the same extent. There were lots of complaints about images, or hypocrisy about Facebook’s enforcement of community standards (such as allowing rape jokes, but deleting pictures of breast-feeding), and other things like that. But the accounts don’t say if they reported it or took action.

This is strange because in cases of offline harassment in the street, where it escalates into something physical like a fight, women are often at a disadvantage: whereas in the online context women ought to have more leverage — but it does’t seem like reporting is being done. When we examined the themes of how people reacted online, we further differentiated between removing the source of a sexist comment (such as unfriending, unfollowing, muting, deleting) and removing the self (such as going offline, or removing yourself from the platform). It seemed that removing the source was generally more common than removing the self.

So people might simply be normalising the idea that misogyny and sexism is going to exist in forums. In the data someone had reported someone on Twitter saying “Well if you’re on Twitter you’re asking for it” — indicative of a “short-skirt” line of thinking about engaging on social media. In this environment people might see unfollowing and unfriending as a form of management and negotiation, as opposed to a fundamental problem with the site itself. It would be interesting to explore the self-censoring that happens before anything happens: quite a few of the examples we read opened with “I wasn’t even wearing anything provocative, but [this] happened..”. And it would be interesting to know if people also think like that in the online context: “I wasn’t even participating in a controversial way, but this still happened”. It’s an interesting parallel, maybe.

The authors surveyed 19,000 men and women from nationally representative samples from the UK, the United States, Canada and Germany, with over half saying they had played internet games recently. Out of the total sample, 1% of young adults (18-24 year olds) and 0.5% of the general population (aged 18 or older) reported symptoms linking play to possible addictive behaviour — less than half of recently reported rates for gambling.

They warn that researchers studying the potential “darker sides” of Internet-based games must be cautious. Extrapolating from their data, as many as a million American adults might meet the proposed DSM-5 criteria for addiction to online games — representing a large cohort of people struggling with what could be clinically dysregulated behavior. However, because the authors found no evidence supporting a clear link to clinical outcomes, they warn that more evidence for clinical and behavioral effects is needed before concluding that this is a legitimate candidate for inclusion in future revisions of the DSM. If adopted, Internet gaming disorder would vie for limited therapeutic resources with a range of serious psychiatric disorders.

Read the full article: Andrew K. Przybylski, Netta Weinstein, Kou Murayama (2016) Internet Gaming Disorder: Investigating the Clinical Relevance of a New Phenomenon. American Journal of Psychiatry. Published online: November 04, 2016.

We caught up with Andy to explore the broader implications of the study:

Ed.: Is “gaming addiction” or “Internet addition” really a thing? e.g. is it something dreamed up by politicians / media people, or is it something that has been discussed and reported by psychiatrists and GPs on the ground?

Andy: Although internet addiction started as a joke about the pathologizing of everyday behaviours, popular fears have put it on the map for policymakers and researchers. In other words, thinking about potential disorders linked to the internet, gaming, and technology have taken on a life of their own.

Ed.: Two years ago the APA identified “a critical need for good research to look into whether internet gamers run a risk of becoming addicted” and asked how such an addiction might be diagnosed properly (i.e. using a checklist of symptoms). What other work or discussion has come out of that call?

Andy: In recent years two groups of researchers have emerged, one arguing there is an international consensus about the potential disorder based on the checklist, the second arguing that it is problematic to pathologize internet gaming. This second group says we don’t understand enough about gaming to know if it’s any different from other hobbies, like being a sports fan. They’re concerned that it could lead other activities to be classified as pathological. Our study set out to test if the checklist approach works, a rigorous test of the APA call for research using the symptoms proposed.

Ed.: Do fears (whether founded or not) of addiction overlap at all with fears of violent video games perhaps altering players’ behaviour? Or are they very clearly discussed and understood as very separate issues?

Andy: Although the fears do converge, the evidence does not. There is a general view that some people might be more liable to be influenced by the addictive or violent aspects of gaming but this remains an untested assumption. In both areas the quality of the evidence base needs critical improvement before the work is valuable for policymakers and mental health professionals.

Ed.: And what’s the broad landscape like in this area – i.e. who are the main players, stakeholders, and pressure points?

Andy: In addition to the American Psychiatric Association (DSM-5), the World Health Organisation is considering formalising Gaming Disorder as a potential mental health issue in the next revision of the International Classifications of Disease (ICD) tool. There is a movement among researchers (myself included based on this research) to urge caution rushing to create new behavioural addition based on gaming for the ICD-11. It is likely that including gaming addiction will do more harm than good by confusing an already complex and under developed research area.

Ed.: And lastly: asking the researcher – do we have enough data and analysis to be able to discuss this sensibly and scientifically? What would a “definitive answer” to this question look like to you — and is it achievable?

Andy: The most important thing to understand about this research area is that there is very little high quality evidence. Generally speaking there are two kinds of empirical studies in the social and clinical sciences, exploratory studies and confirmatory ones. Most of the evidence about gaming addiction to date is exploratory, that is the analyses reported represent what ‘sticks to the wall’ after the data is collected. This isn’t a good evidence for health policy.

Our studies represent the first confirmatory research on gaming addiction. We pre-registered how we were going to collect and analyse our data before we saw it. We collected large representative samples and tested a priori hypotheses. This makes a big difference in the kinds of inferences you can draw and the value of the work to policymakers. We hope our work represents the first of many studies on technology effects that put open data, open code, and a pre-registered analysis plans at the centre of science in this area. Until the research field adopts these high standards we will not have accurate definitive answers about Internet Gaming Disorder.

Read the full article: Andrew K. Przybylski, Netta Weinstein, Kou Murayama (2016) Internet Gaming Disorder: Investigating the Clinical Relevance of a New Phenomenon. American Journal of Psychiatry. Published online: November 04, 2016.

Andy was talking to David Sutcliffe, Managing Editor of the Policy blog.

Online support groups are one of the major ways in which the Internet has fundamentally changed how people experience health and health care. They provide a platform for health discussions formerly restricted by time and place, enable individuals to connect with others in similar situations, and facilitate open, anonymous communication.

Previous studies have identified that individuals primarily obtain two kinds of support from online support groups: informational (for example, advice on treatments, medication, symptom relief, and diet) and emotional (for example, receiving encouragement, being told they are in others’ prayers, receiving “hugs”, or being told that they are not alone). However, existing research has been limited as it has often used hand-coded qualitative approaches to contrast both forms of support, thereby only examining relatively few posts (<1,000) for one or two conditions.

In contrast, our research employed a machine-learning approach suitable for uncovering patterns in “big data”. Using this method a computer (which initially has no knowledge of online support groups) is given examples of informational and emotional posts (2,000 examples in our study). It then “learns” what words are associated with each category (emotional: prayers, sorry, hugs, glad, thoughts, deal, welcome, thank, god, loved, strength, alone, support, wonderful, sending; informational: effects, started, weight, blood, eating, drink, dose, night, recently, taking, side, using, twice, meal). The computer then uses this knowledge to assess new posts, and decide whether they contain more emotional or informational support.

With this approach we were able to determine the emotional or informational content of 40,000 posts across 14 different health conditions (breast cancer, prostate cancer, lung cancer, depression, schizophrenia, Alzheimer’s disease, multiple sclerosis, cystic fibrosis, fibromyalgia, heart failure, diabetes type 2, irritable bowel syndrome, asthma, and chronic obstructive pulmonary disease) on the international support group forum Dailystrength.org.

Our research revealed a slight overall tendency towards emotional posts (58% of posts were emotionally oriented). Across all diseases, those who write more also tend to write more emotional posts—we assume that as people become more involved and build relationships with other users they tend to provide more emotional support, instead of simply providing information in one-off interactions. At the same time, we also observed that older people write more informational posts. This may be explained by the fact that older people more generally use the Internet to find information, that they become experts in their chronic conditions over time, and that with increasing age health conditions may have less emotional impact as they are relatively more expected.

The demographic prevalence of the condition may also be enmeshed with the disease-related tendency to write informational or emotional posts. Our analysis suggests that content differs across the 14 conditions: mental health or brain-related conditions (such as depression, schizophrenia, and Alzheimer’s disease) feature more emotionally oriented posts, with around 80% of posts primarily containing emotional support. In contrast, nonterminal physical conditions (such as irritable bowel syndrome, diabetes, asthma) rather focus on informational support, with around 70% of posts providing advice about symptoms, treatments, and medication.

Finally, there was no gender difference across conditions with respect to the amount of posts that were informational versus emotional. That said, prostate cancer forums are oriented towards informational support, whereas breast cancer forums feature more emotional support. Apart from the generally different nature of both conditions, one explanation may lie in the nature of single-gender versus mixed-gender groups: an earlier meta-study found that women write more emotional content than men when talking among others of the same gender – but interestingly, in mixed-gender discussions, these differences nearly disappeared.

Our research helped to identify factors that determine whether online content is informational or emotional, and demonstrated how posts differ across conditions. In addition to theoretical insights about patient needs, this research will help practitioners to better understand the role of online support groups for different patients, and to provide advice to patients about the value of online support.

The results also suggest that online support groups should be integrated into the digital health strategies of the UK and other nations. At present the UK plan for “Personalised Health and Care 2020” is centred around digital services provided within the health system, and does not yet reflect the value of person-generated health data from online support groups to patients. Our research substantiates that it would benefit from considering the instrumental role that online support groups can play in the healthcare process.

Read the full paper: Deetjen, U. and J. A. Powell (2016) Informational and emotional elements in online support groups: a Bayesian approach to large-scale content analysis. Journal of the American Medical Informatics Association. http://dx.doi.org/10.1093/jamia/ocv190

Ulrike Deetjen (née Rauer) is a doctoral student at the Oxford Internet Institute researching the influence of the Internet on healthcare provision and health outcomes.

Dementia affects about 44 million individuals, a number that is expected to nearly double by 2030 and triple by 2050. With an estimated annual cost of USD 604 billion, dementia represents a major economic burden for both industrial and developing countries, as well as a significant physical and emotional burden on individuals, family members and caregivers. There is currently no cure for dementia or a reliable way to slow its progress, and the G8 health ministers have set the goal of finding a cure or disease-modifying therapy by 2025. However, the underlying mechanisms are complex, and influenced by a range of genetic and environmental influences that may have no immediately apparent connection to brain health.

Of course medical research relies on access to large amounts of data, including clinical, genetic and imaging datasets. Making these widely available across research groups helps reduce data collection efforts, increases the statistical power of studies and makes data accessible to more researchers. This is particularly important from a global perspective: Swedish researchers say, for example, that they are sitting on a goldmine of excellent longitudinal and linked data on a variety of medical conditions including dementia, but that they have too few researchers to exploit its potential. Other countries will have many researchers, and less data.

‘Big data’ adds new sources of data and ways of analysing them to the repertoire of traditional medical research data. This can include (non-medical) data from online patient platforms, shop loyalty cards, and mobile phones — made available, for example, through Apple’s ResearchKit, just announced last week. As dementia is believed to be influenced by a wide range of social, environmental and lifestyle-related factors (such as diet, smoking, fitness training, and people’s social networks), and this behavioural data has the potential to improve early diagnosis, as well as allow retrospective insights into events in the years leading up to a diagnosis. For example, data on changes in shopping habits (accessible through loyalty cards) may provide an early indication of dementia.

However, there are many challenges to using and sharing big data for dementia research. The technology hurdles can largely be overcome, but there are also deep-seated issues around the management of data collection, analysis and sharing, as well as underlying people-related challenges in relation to skills, incentives, and mindsets. Change will only happen if we tackle these challenges at all levels jointly.

As data are combined from different research teams, institutions and nations — or even from non-medical sources — new access models will need to be developed that make data widely available to researchers while protecting the privacy and other interests of the data originator. Establishing robust and flexible core data standards that make data more sharable by design can lower barriers for data sharing, and help avoid researchers expending time and effort trying to establish the conditions of their use.

At the same time, we need policies that protect citizens against undue exploitation of their data. Consent needs to be understood by individuals — including the complex and far-reaching implications of providing genetic information — and should provide effective enforcement mechanisms to protect them against data misuse. Privacy concerns about digital, highly sensitive data are important and should not be de-emphasised as a subordinate goal to advancing dementia research. Beyond releasing data in a protected environments, allowing people to voluntarily “donate data”, and making consent understandable and enforceable, we also need governance mechanisms that safeguard appropriate data use for a wide range of purposes. This is particularly important as the significance of data changes with its context of use, and data will never be fully anonymisable.

We also need a favourable ecosystem with stable and beneficial legal frameworks, and links between academic researchers and private organisations for exchange of data and expertise. Legislation needs to account of the growing importance of global research communities in terms of funding and making best use of human and data resources. Also important is sustainable funding for data infrastructures, as well as an understanding that funders can have considerable influence on how research data, in particular, are made available. One of the most fundamental challenges in terms of data sharing is that there are relatively few incentives or career rewards that accrue to data creators and curators, so ways to recognise the value of shared data must be built into the research system.

In terms of skills, we need more health-/bioinformatics talent, as well as collaboration with those disciplines researching factors “below the neck”, such as cardiovascular or metabolic diseases, as scientists increasingly find that these may be associated with dementia to a larger extent than previously thought. Linking in engineers, physicists or innovative private sector organisations may prove fruitful for tapping into new skill sets to separate the signal from the noise in big data approaches.

In summary, everyone involved needs to adopt a mindset of responsible data sharing, collaborative effort, and a long-term commitment to building two-way connections between basic science, clinical care and the healthcare in everyday life. Fully capturing the health-related potential of big data requires “out of the box” thinking in terms of how to profit from the huge amounts of data being generated routinely across all facets of our everyday lives. This sort of data offers ways for individuals to become involved, by actively donating their data to research efforts, participating in consumer-led research, or engaging as citizen scientists. Empowering people to be active contributors to science may help alleviate the common feeling of helplessness faced by those whose lives are affected by dementia.

Of course, to do this we need to develop a culture that promotes trust between the people providing the data and those capturing and using it, as well as an ongoing dialogue about new ethical questions raised by collection and use of big data. Technical, legal and consent-related mechanisms to protect individual’s sensitive biomedical and lifestyle-related data against misuse may not always be sufficient, as the recent Nuffield Council on Bioethics report has argued. For example, we need a discussion around the direct and indirect benefits to participants of engaging in research, when it is appropriate for data collected for one purpose to be put to others, and to what extent individuals can make decisions particularly on genetic data, which may have more far-reaching consequences for their own and their family members’ professional and personal lives if health conditions, for example, can be predicted by others (such as employers and insurance companies).

Policymakers and the international community have an integral leadership role to play in informing and driving the public debate on responsible use and sharing of medical data, as well as in supporting the process through funding, incentivising collaboration between public and private stakeholders, creating data sharing incentives (for example, via taxation), and ensuring stability of research and legal frameworks.

Dementia is a disease that concerns all nations in the developed and developing world, and just as diseases have no respect for national boundaries, neither should research into dementia (and the data infrastructures that support it) be seen as a purely national or regional priority. The high personal, societal and economic importance of improving the prevention, diagnosis, treatment and cure of dementia worldwide should provide a strong incentive for establishing robust and safe mechanisms for data sharing.

Read the full report: Deetjen, U., E. T. Meyer and R. Schroeder (2015) Big Data for Advancing Dementia Research. Paris, France: OECD Publishing.

Ed: Does research on MOOCs differ in any way from existing research on online learning?

Rebecca: Despite the hype around MOOCs to date, there are many similarities between MOOC research and the breadth of previous investigations into (online) learning. Many of the trends we’ve observed (the prevalence of forum lurking; community formation; etc.) have been studied previously and are supported by earlier findings. That said, the combination of scale, global-reach, duration, and “semi-synchronicity” of MOOCs have made them different enough to inspire this work. In particular, the optional nature of participation among a global-body of lifelong learners for a short burst of time (e.g. a few weeks) is a relatively new learning environment that, despite theoretical ties to existing educational research, poses a new set of challenges and opportunities.

Ed: The MOOC forum networks you modelled seemed to be less efficient at spreading information than randomly generated networks. Do you think this inefficiency is due to structural constraints of the system (or just because inefficiency is not selected against); or is there something deeper happening here, maybe saying something about the nature of learning, and networked interaction?

Rebecca: First off, it’s important to not confuse the structural “inefficiency” of communication with some inherent learning “inefficiency”. The inefficiency in the sub-forums is a matter of information diffusion—i.e., because there are communities that form in the discussion spaces, these communities tend to “trap” knowledge and information instead of promoting the spread of these ideas to a vast array of learners. This information diffusion inefficiency is not necessarily a bad thing, however. It’s a natural human tendency to form communities, and there is much education research that says learning in small groups can be much more beneficial / effective than large-scale learning. The important point that our work hopes to make is that the existence and nature of these communities seems to be influenced by the types of topics that are being discussed (and vice versa)—and that educators may be able to cultivate more isolated or inclusive network dynamics in these course settings by carefully selecting and presenting these different discussion topics to learners.

Ed: Drawing on surveys and learning outcomes you could categorise four ‘learner types’, who tend to behave differently in the network. Could the network be made more efficient by streaming groups by learning objective, or by type of interaction (eg learning / feedback / social)?

Rebecca: Given our network vulnerability analysis, it appears that discussions that focus on problems or issues that are based in real life examples –e.g., those that relate to case studies of real companies and analyses posted by learners of these companies—tend to promote more inclusive engagement and efficient information diffusion. Given that certain types of learners participate in these discussions, one could argue that forming groups around learning preferences and objectives could promote more efficient communications. Still, it’s important to be aware of the potential drawbacks to this, namely, that promoting like-minded / similar people to interact with those they are similar to could further prevent “learning through diverse exposures” that these massive-scale settings can be well-suited to promote.

Ed: In the classroom, the teacher can encourage participation and discussion if it flags: are there mechanisms to trigger or seed interaction if the levels of network activity fall below a certain threshold? How much real-time monitoring tends to occur in these systems?

Rebecca: Yes, it appears that educators may be able to influence or achieve certain types of network patterns. While each MOOC is different (some course staff members tend to be much more engaged than others, learners may have different motivations, etc.), on the whole, there isn’t much real-time monitoring in MOOCs, and MOOC platforms are still in early days where there is little to no automated monitoring or feedback (beyond static analytics dashboards for instructors).

Ed: Does learner participation in these forums improve outcomes? Do the most central users in the interaction network perform better? And do they tend to interact with other very central people?

Rebecca: While we can’t infer causation, we found that when compared to the entire course, a significantly higher percentage of high achievers were also forum participants. The more likely explanation for this is that those who are committed to completing the course and performing well also tend to use the forums—but the plurality of forum participants (44% in one of the courses we analyzed) are actually those that “fail” by traditional marks (receive below 50% in the course). Indeed, many central users tend to be those that are simply auditing the course or who are interested in communicating with others without any intention of completing course assignments. These central users tend to communicate with other central users, but also, with those whose participation is much sparser / “on the fringes”.

Ed: Slightly facetiously: you can identify ‘central’ individuals in the network who spark and sustain interaction. Can you also find people who basically cause interaction to die? Who will cause the network to fall apart? And could you start to predict the strength of a network based on the profiles and proportions of the individuals who make it up?

Rebecca: It is certainly possible to further explore how different people seem. One way this can be achieved is by exploring the temporal dynamics at play—e.g., by visualizing the communication network at any point in time and creating network “snapshots” at every hour or day, or perhaps, with every new participant, to observe how the trends and structures evolve. While this method still doesn’t allow us to identify the exact influence of any given individual’s participation (since there are so many other confounding factors, for example, how far into the course it is, peoples’ schedules / lives outside of the MOOC, etc.), it may provide some insight into their roles. We could of course define some quantitative measure(s) to measure “network strength” based on learner profiles, but caution against overarching or broad claims in doing so due to confounding forces would be essential.

Ed: The majority of my own interactions are mediated by a keyboard: which is actually a pretty inefficient way of communicating, and certainly a terrible way of arguing through a complex point. Is there any sense from MOOCs that text-based communication might be a barrier to some forms of interaction, or learning?

Rebecca: This is an excellent observation. Given the global student body, varying levels of comfort in English (and written language more broadly), differing preferences for communication, etc., there is much reason to believe that a lack of participation could result from a lack of comfort with the keyboard (or written communication more generally). Indeed, in the MOOCs we’ve studied, many learners have attempted to meet up on Google Hangouts or other non-text based media to form and sustain study groups, suggesting that many learners seek to use alternative technologies to interact with others and achieve their learning objectives.

Ed: Based on this data and analysis, are there any obvious design points that might improve interaction efficiency and learning outcomes in these platforms?

Rebecca: As I have mentioned already, open-ended questions that focus on real-life case studies tend to promote the least vulnerable and most “efficient” discussions, which may be of interest to practitioners looking to cultivate these sorts of environments. More broadly, the lack of sustained participation in the forums suggests that there are a number of “forces of disengagement” at play, one of them being that the sheer amount of content being generated in the discussion spaces (one course had over 2,700 threads and 15,600 posts) could be contributing to a sense of “content overload” and helplessness for learners. Designing platforms that help mitigate this problem will be fundamental to the vitality and effectiveness of these learning spaces in the future.

Ed: I suppose there is an inherent tension between making the online environment very smooth and seductive, and the process of learning; which is often difficult and frustrating: the very opposite experience aimed for (eg) by games designers. How do MOOCs deal with this tension? (And how much gamification is common to these systems, if any?)

Rebecca: To date, gamification seems to have been sparse in most MOOCs, although there are some interesting experiments in the works. Indeed, one study (Anderson et al., 2014) used a randomized control trial to add badges (that indicate student engagement levels) next to the names of learners in MOOC discussion spaces in order to determine if and how this affects further engagement. Coursera has also started to publicly display badges next to the names of learners that have signed up for the paid Signature Track of a specific course (presumably, to signal which learners are “more serious” about completing the course than others). As these platforms become more social (and perhaps career advancement-oriented), it’s quite possible that gamification will become more popular. This gamification may not ease the process of learning or make it more comfortable, but rather, offer additional opportunities to mitigate the challenges massive-scale anonymity and lack of information about peers to facilitate more social learning.

Ed: How much of this work is applicable to other online environments that involve thousands of people exploring and interacting together: for example deliberation, crowd production and interactive gaming, which certainly involve quantifiable interactions and a degree of negotiation and learning?

Rebecca: Since MOOCs are so loosely structured and could largely be considered “informal” learning spaces, we believe the engagement dynamics we’ve found could apply to a number of other large-scale informal learning/interactive spaces online. Similar crowd-like structures can be found in a variety of policy and practice settings.

Ed: This project has adopted a mixed methods approach: what have you gained by this, and how common is it in the field?

Rebecca: Combining computational network analysis and machine learning with qualitative content analysis and in-depth interviews has been one of the greatest strengths of this work, and a great learning opportunity for the research team. Often in empirical research, it is important to validate findings across a variety of methods to ensure that they’re robust. Given the complexity of human subjects, we knew computational methods could only go so far in revealing underlying trends; and given the scale of the dataset, we knew there were patterns that qualitative analysis alone would not enable us to detect. A mixed-methods approach enabled us to simultaneously and robustly address these dimensions. MOOC research to date has been quite interdisciplinary, bringing together computer scientists, educationists, psychologists, statisticians, and a number of other areas of expertise into a single domain. The interdisciplinarity of research in this field is arguably one of the most exciting indicators of what the future might hold.

Ed: As well as the network analysis, you also carried out interviews with MOOC participants. What did you learn from them that wasn’t obvious from the digital trace data?

Rebecca: The interviews were essential to this investigation. In addition to confirming the trends revealed by our computational explorations (which revealed the what of the underlying dynamics at play), the interviews, revealed much of the why. In particular, we learned people’s motivations for participating in (or disengaging from) the discussion forums, which provided an important backdrop for subsequent quantitative (and qualitative) investigations. We have also learned a lot more about people’s experiences of learning, the strategies they employ to their support their learning and issues around power and inequality in MOOCs.

Ed: You handcoded more than 6000 forum posts in one of the MOOCs you investigated. What findings did this yield? How would you characterise the learning and interaction you observed through this content analysis?

Rebecca: The qualitative content analysis of over 6,500 posts revealed several key insights. For one, we confirmed (as the network analysis suggested), that most discussion is insignificant “noise”—people looking to introduce themselves or have short-lived discussions about topics that are beyond the scope of the course. In a few instances, however, we discovered the different patterns (and sometimes, cycles) of knowledge construction that can occur within a specific discussion thread. In some cases, we found that discussion threads grew to be so long (with over hundreds of posts), that topics were repeated or earlier posts disregarded because new participants didn’t read and/or consider them before adding their own replies.

Ed: How are you planning to extend this work?

Rebecca: As mentioned already, feelings of helplessness resulting from sheer “content overload” in the discussion forums appear to be a key force of disengagement. To that end, as we now have a preliminary understanding of communication dynamics and learner tendencies within these sorts of learning environments, we now hope to leverage this background knowledge to develop new methods for promoting engagement and the fulfilment of individual learning objectives in these settings—in particular, by trying to mitigate the “content overload” issues in some way. Stay tuned for updates

References

Anderson, A., Huttenlocher, D., Kleinberg, J. & Leskovec, J., Engaging with Massive Open Online Courses.  In: WWW ’14 Proceedings of the 23rd International World Wide Web Conference, Seoul, Korea. New York: ACM (2014).

Read the full paper: Gillani, N., Yasseri, T., Eynon, R., and Hjorth, I. (2014) Structural limitations of learning in a crowd – communication vulnerability and information diffusion in MOOCs. Scientific Reports 4.

Rebecca Eynon was talking to blog editor David Sutcliffe.

Rebecca Eynon holds a joint academic post between the Oxford Internet Institute (OII) and the Department of Education at the University of Oxford. Her research focuses on education, learning and inequalities, and she has carried out projects in a range of settings (higher education, schools and the home) and life stages (childhood, adolescence and late adulthood).

In the realm of education and technology, a central question that researchers and policymakers alike have been grappling with has been: why does there continue to be such vast amounts of resources invested in education technology and related initiatives without substantial evidence to suggest that the promises of such technologies and related initiatives are being fulfilled? By adopting a political economy approach, which examines the social, political and economic processes shaping the production, consumption, and distribution of resources including information and communication technologies (Mosco, 2009), we can begin to understand why and how the considerable zeal surrounding education technologies and the sustained investments persist.

An exemplar case for this type of analysis, giving us a deeper understanding of the structural forces shaping the K-12 institutional circuits, would be the recent tech-centered incidents riddling the Los Angeles Unified School District.

iPad-for-all and the MiSiS CriSiS

Last month the Los Angeles Unified School District Superintendent, John Deasy, and Chief Technology Officer, Ron Chandler, both resigned due to the $1 billion iPad initiative and what is being called the MiSiS CriSiS. Underpinning these initiatives are idealistic beliefs in the powers of technology and the trend towards the standardization and corporatization of the US K-12 education.

Despite the dire need for classroom upgrades and recovery from the recession-induced mass teacher layoffs and library closures, this past year John Deasy announced the plan to direct the district’s resources toward an initiative that places iPads in all 640,000 LAUSD students’ hands. Perpetuating the idealistic promise that technology acts as a leveling tool in society, Deasy pledged that this initiative would afford equal educational opportunities across the board regardless of race or socioeconomic background of students. He stated that this would allow low-income students to have access to the same technological tools as their middle class counterparts. Commendable as the effort was, this overly idealized sentiment that technology will ameliorate the deeply rooted systemic inequities facing society is partly responsible for the furthering of misdirected investments and ineffective policies in the education technology realm.

The My Integrated Student Information System was meant to streamline the course registration process and centralize the storage of all student records. For reasons that haven’t been entirely unearthed yet, the software was pushed by Deasy and was launched a couple months ago despite various warnings from administrators and teachers that it wasn’t ready. Leaving many students disenfranchised without the necessary courses needed for college and unverified accuracy of senior transcripts for college applications, the MiSiS CriSiS brings to light one of the main concerns with these technological integration projects in schools and the interests involved — accountability. Who is accountable for ensuring there is a back up of records or to get students into the classes they need? Who is accountable for ensuring the most reliable software is chosen? While there has been heightened accountability measures in the form of high-stakes testing directed towards teacher effectiveness, there still remains little accountability regarding the process of choosing specific technology services, their plans for implementation, and overall effectiveness of the services or initiatives.

These incidents are direct results of the broader political-economic structure of public education in the United States. This is a structure characterized by interrelationships between the federal, state, and local governments and the private sector. Central to this institutional structure and the proliferation of ICT-related education initiatives are the workings of digital capitalism[1].

Digital Capitalism and the American Educational-Industrial Complex[2]

Federal, state, and local governments all contribute funding to K-12 public education in the U.S. but according to the U.S. constitution, states are ultimately responsible for their public schools. The bulk of funding for schools comes from local taxes, which creates vast inequalities across the country in terms of educational resources, infrastructure, and teaching. There has been sustained effort from the federal level to ameliorate these inequities in education, from the Elementary and Secondary Education Act of 1965 and its subsequent amendments to the highly contentious Race to the Top initiative that has built-in incentives for underachieving schools to increase student performance. However, these efforts are exceptionally limited and riddled with problems. This has only been complicated further with the economic downturn in the US, which has resulted in massive layoffs in public education resulting in an unrelenting and growing dependence on private sector resources.

Accountability, as mentioned above, is a major theme discussed in the context of education and digital capitalism. Again, while initiatives like No Child Left Behind and Race to the Top are meant to hold teachers accountable, there is still a lack of accountability measures for private sector involvement in education. Another force of digital capitalism that is responsible for the sustained proliferation of ICT-driven initiatives in education is the shift in the perceived goals of education from serving to ensure an enlightened citizenry to being valued for its vocational outputs. This is being fueled by global competitiveness discourses presented under the veil of “21^st century skills” rhetoric in order to keep the U.S. economically competitive. This has made the circular relationship between government and the corporate technology sector more salient in education. Simply put, the government is putting pressure on the public to ensure new generations are primed with the necessary 21^st century skills in order to participate in the labor market while also enhancing the position of the US in the global economy and the private sector is pushing this discourse further because they have products to sell to fulfill this elusive goal. This is further exemplified in the new standardized curriculum project rolling out in K-12.

Technology Driven Corporatization of American Public Education

This past year 45 states adopted the Common Core State Standards, which is meant to standardize curriculum across the country under the assumption that it will place all students regardless of race or class at the same level across all subjects. One major technological implication of this standardization includes the standardization of delivery systems, signaled by the iPads with Pearson education software project in LAUSD. This creates a direct line of entry for private companies to become even further entrenched in education. In many ways, these developments are making the privatization efforts more concrete and foreshadowing the evolving structure of the public K-12 system. Privatization doesn’t necessarily mean that schools are going to be under direct control of private companies (although this is already happening in certain parts of the country; you can read about it in my forthcoming article) but it does represent the transformation of education as a public good into a profit center for private interests, as demonstrated in LAUSD.

In the LAUSD situation, it is no coincidence that John Deasy has an extensive background in private industry and specifically education technology industry. Deasy came from the Gates Foundation, one of the leading education partners. Former Deputy Superintendent Jamie Aquino came from Pearson, the curriculum developers that were to provide the software for the iPad initiative. What’s more significant about these close ties and the iPad for all project is that before the bidding went public, Aquino and Deasy had already begun a backdoor deal with both Pearson and Apple to carry out the initiative thus illustrating the conflicts of interests between government, industry, and education.

Conclusion

Perhaps the recent incidents that have been riddling the Los Angeles Unified School District will bring more public attention to these issues and a push for more evidenced-based policies will emerge. Nonetheless, the issues arising in LA Unified represent broader structural, political-economic forces that shed light on the answer to the question posed earlier. Attention to these larger structural processes being propelled by such forces is what drives my own research, which aims to extend digital exclusion scholarship and provide evidence-based suggestions for more sustainable policies that maximize benefits for the populations they seek to serve. In this vein, posing a couple of preliminary policy suggestions might be appropriate.

In a nutshell, it would be naïve to assume that the private sector has nothing to offer the public sector and that public schools are not in a position to benefit from the resources they bring. However, the decision-making power needs to be more balanced among all stakeholders in order for these benefits to be realized. Several suggestions for policymakers are made in an article based on my previous research that is forthcoming but can similarly be applied here. Overall, there is a need for impact assessment measurements for these interventions that would provide valuable insight into the effectiveness of the ICT-driven initiatives. Additionally, a recommendation for more accountability measures in each stage of these projects is needed in order to ensure that the benefits are being realized and promises fulfilled. Ultimately, while perhaps a bit idealistic at this point, a shift from an economic focus to a social rights based approach to policymaking in this realm would help to create more sustainable policies that maximize the benefits for groups they’re meant to serve.

Admittedly, this is a simplified overview of the forces at play in the current restructuring of K-12 but hopefully it has provided useful insight into how technology’s role in society is not determined by the technology itself but rather by a complex ecosystem of networks and power relations that shape larger social structures. It is, of course, much more complex with many layers of discourses and sociopolitical entanglements but my goal is that this snapshot has highlighted the importance for understanding the social, political, and economic underpinnings in order to grasp the larger picture of technology’s role in society and, more specifically, education.

References

Mosco, V. (2009). The Political Economy of Communication. London. Sage Publishing.

Picciano, A.G., & Spring, J.H. (2013). The Great American Education-Industrial Complex: Ideology, technology, and profit. Routledge.

Schiller, D. (1999) Digital Capitalism. Cambridge: The MIT Press.

[1] For more on Digital Capitalism, see Dan Schiller (1999) Digital Capitalism.

[2] This concept was drawn from Picciano and Spring’s (2014) The Great American Education-Industrial Complex: Ideology, Technology, and profit.

Paige Mustain is a DPhil student at the Oxford Internet Institute. Her research lies at the intersection of education and digital exclusion. More specifically, she focuses on the political economy of information and communication technology (ICT) development initiatives in the realm of education.

But what constitutes a “significant” learning interaction? In large-scale MOOC forums, with socio-culturally diverse learners with different motivations for participating, this is a non-trivial problem. MOOCs are best defined as “non-formal” learning spaces, where learners pick and choose how (and if) they interact. This kind of group membership, together with the short-term nature of these courses, means that relatively weak inter-personal relationships are likely. Many of the tens of thousands of interactions in the forum may have little relevance to the learning process. So can we actually define the underlying network of significant interactions? Only once we have done this can we explore firstly how information flows through the forums, and secondly the robustness of those interaction networks: in short, the effectiveness of the platform design for supporting group learning at scale.

To explore these questions, we analysed data from 167,000 students registered on two business MOOCs offered on the Coursera platform. Almost 8000 students contributed around 30,000 discussion posts over the six weeks of the courses; almost 30,000 students viewed at least one discussion thread, totalling 321,769 discussion thread views. We first modelled these communications as a social network, with nodes representing students who posted in the discussion forums, and edges (ie links) indicating co-participation in at least one discussion thread. Of course, not all links will be equally important: many exchanges will be trivial (‘hello’, ‘thanks’ etc.). Our task, then, was to derive a “true” network of meaningful student interactions (ie iterative, consistent dialogue) by filtering out those links generated by random encounters (Figure 1; see also full paper for methodology).

Network vulnerability is interesting because it indicates how integrated and inclusive the communication flow is. Discussion forums with fleeting participation will have only a very few vocal participants: removing these people from the network will markedly reduce the information flow between the other participants — as the network falls apart, it simply becomes more difficult for information to travel across it via linked nodes. Conversely, forums that encourage repeated engagement and in-depth discussion among participants will have a larger ‘critical set’, with discussion distributed across a wide range of learners.

To understand the structure of group communication in the two courses, we looked at how quickly our modelled communication network fell apart when: (a) the most central nodes were iteratively disconnected (Figure 2; blue), compared with when (b) nodes were removed at random (ie the ‘neutral’ case; green). In the random case, the network degrades evenly, as expected. When we selectively remove the most central nodes, however, we see rapid disintegration: indicating the presence of individuals who are acting as important ‘bridges’ across the network. In other words, the network of student interactions is not random: it has structure.

Of course, the structure of participant interactions will reflect the purpose and design of the particular forum. We can see from Figure 3 that different forums in the courses have different vulnerability thresholds. Forums with high levels of iterative dialogue and knowledge construction — with learners sharing ideas and insights about weekly questions, strategic analyses, or course outcomes — are the least vulnerable to degradation. A relatively high proportion of nodes have to be removed before the network falls apart (rightmost-blue line). Forums where most individuals post once to introduce themselves and then move their discussions to other platforms (such as Facebook) or cease engagement altogether tend to be more vulnerable to degradation (left-most blue line). The different vulnerability thresholds suggest that different topics (and forum functions) promote different levels of forum engagement. Certainly, asking students open-ended questions tended to encourage significant discussions, leading to greater engagement and knowledge construction as they read analyses posted by their peers and commented with additional insights or critiques.

Understanding something about the vulnerability of a communication or interaction network is important, because it will tend to affect how information spreads across it. To investigate this, we simulated an information diffusion model similar to that used to model social contagion. Although simplistic, the SI model (‘susceptible-infected’) is very useful in analyzing topological and temporal effects on networked communication systems. While the model doesn’t account for things like decaying interest over time or peer influence, it allows us to compare the efficiency of different network topologies.

We compared our (real-data) network model with a randomized network in order to see how well information would flow if the community structures we observed in Figure 2 did not exist. Figure 4 shows the number of ‘infected’ (or ‘reached’) nodes over time for both the real (solid lines) and randomized networks (dashed lines). In all the forums, we can see that information actually spreads faster in the randomised networks. This is explained by the existence of local community structures in the real-world networks: networks with dense clusters of nodes (i.e. a clumpy network) will result in slower diffusion than a network with a more even distribution of communication, where participants do not tend to favor discussions with a limited cohort of their peers.

Overall, these results reveal an important characteristic of student discussion in MOOCs: when it comes to significant communication between learners, there are simply too many discussion topics and too much heterogeneity (ie clumpiness) to result in truly global-scale discussion. Instead, most information exchange, and by extension, any knowledge construction in the discussion forums occurs in small, short-lived groups: with information “trapped” in small learner groups. This finding is important as it highlights structural limitations that may impact the ability of MOOCs to facilitate communication amongst learners that look to learn “in the crowd”.

These insights into the communication dynamics motivate a number of important questions about how social learning can be better supported, and facilitated, in MOOCs. They certainly suggest the need to leverage intelligent machine learning algorithms to support the needs of crowd-based learners; for example, in detecting different types of discussion and patterns of engagement during the runtime of a course to help students identify and engage in conversations that promote individualized learning. Without such interventions the current structural limitations of social learning in MOOCs may prevent the realization of a truly global classroom.

The next post addresses qualitative content analysis and how machine-learning community detection schemes can be used to infer latent learner communities from the content of forum posts.

Read the full paper: Gillani, N., Yasseri, T., Eynon, R., and Hjorth, I. (2014) Structural limitations of learning in a crowd – communication vulnerability and information diffusion in MOOCs. Scientific Reports 4.

Rebecca Eynon holds a joint academic post between the Oxford Internet Institute (OII) and the Department of Education at the University of Oxford. Her research focuses on education, learning and inequalities, and she has carried out projects in a range of settings (higher education, schools and the home) and life stages (childhood, adolescence and late adulthood).

Of course, the focus on risk rather than harm is understandable from an ethical and methodological perspective. It wouldn’t be ethical, for example, to conduct a trial in which one group of children was deliberately exposed to very violent or sexual content to observe whether any harms resulted. Similarly, surveys can ask respondents to self-report harms experienced online, perhaps through the lens of upsetting images or experiences. But again, there are ethical concerns about adding to children’s distress by questioning them extensively on difficult experiences, and in a survey context it’s also difficult to avoid imposing adult conceptions of ‘harm’ through the wording of the questions.

Despite these difficulties, there are many research projects that aim to measure and understand the relationship between various types of physical, emotional or psychological harm and activities online, albeit often outside the social sciences. With support from the OUP Fell Fund, I worked with colleagues Vera Slavtcheva-Petkova and Monica Bulger to review the extent of evidence available across these other disciplines. Looking at journal articles published between 1997 and 2012, we aimed to identify any empirical evidence detailing Internet-related harms experienced by children and adolescents and to gain a sense of the types of harm recorded, their severity and frequency.

Our findings demonstrate that there are many good studies out there which do address questions of harm, rather than just risk. The narrowly drawn search found 148 empirical studies which either clearly delineated evidence of very specific harms, or offered some evidence of less well-defined harms. Further, these studies offer rich insights into three broad types of harm: health-related (including harms relating to the exacerbation of eating disorders, self-harming behaviour and suicide attempts); sex-related (largely focused on studies of online solicitation and child abuse); and bullying-related (including the effects on mental health and behaviour). Such a range of coverage would come as no surprise to most researchers focusing on children’s Internet use – these are generally well-documented areas, albeit with the focus more normally on risk rather than harm. Perhaps more surprising was the absence in our search of evidence of harm in relation to privacy violations or economic well-being, both of which are increasingly discussed as significant concerns or risks for minors using the Internet. This gap might have been a factor of our search terms, of course, but given the policy relevance of both issues, more empirical study of not just risk but actual harm would seem to be merited in these areas.

Another important gap in the literature concerned the absence of literature demonstrating that severe harms often befall those without prior evidence of vulnerability or risky behaviour. For example, in relation to websites promoting self-harm or eating disorders, there is little evidence that young people previously unaffected by self-harm or eating disorders are influenced by these websites. This isn’t unexpected – other researchers have shown that harm more often befalls those who display riskier behaviour, but this is important to bear in mind when devising treatment or policy strategies for reducing such harms.

It’s also worth noting how difficult it is to determine the prevalence of harms. The best-documented cases are often those where medical, police or court records provide great depth of qualitative detail about individual suffering in cases of online grooming and abuse, eating disorders or self-harm. Yet these cases provide little insight into prevalence. And whilst survey research offers more sense of scale, we found substantial disparities in the levels of harm reported on some issues, with the prevalence of cyber-bullying, for example, varying from 9% to 72% across studies with similar age groups of children. It’s also clear that we quite simply need much more research and policy attention on certain issues. The studies relating to the online grooming of children and production of abuse images are an excellent example of how a broad research base can make an important contribution to our understanding of online risks and harms. Here, journal articles offered a remarkably rich understanding, drawing on data from police reports, court records or clinical files as well as surveys and interviews with victims, perpetrators and carers. There would be real benefits to taking a similarly thorough approach to the study of users of pro-eating disorder, self-harm and pro-suicide websites.

Our review flagged up some important lessons for policy-makers. First, whilst we (justifiably) devote a wealth of resources to the small proportion of children experiencing severe harms as a result of online experiences, the number of those experiencing more minor harms such as those caused by online bullying is likely much higher and may thus deserve more attention than currently received. Second, the diversity of topics discussed and types of harm identified seems to suggest that a one-size-fits-all solution will not work when it comes to online protection of minors. Simply banning or filtering all potentially harmful websites, pages or groups might be more damaging than useful if it drives users to less public means of communicating. Further, whilst some content such as child sexual abuse images are clearly illegal and generate great harms, other content and sites is less easy to condemn if the balance between perpetuating harmful behavior and provide valued peer support is hard to call. It should also be remembered that the need to protect young people from online harms must always be balanced against the need to protect their rights (and opportunities) to freely express themselves and seek information online.

Finally, this study makes an important contribution to public debates about child online safety by reminding us that risk and harm are not equivalent and should not be conflated. More children and young people are exposed to online risks than are actually harmed as a result and our policy responses should reflect this. In this context, the need to protect minors from online harms must always be balanced against their rights and opportunities to freely express themselves and seek information online.

A more detailed account of our findings can be found in this Information, Communication and Society journal article: Evidence on the extent of harms experienced by children as a result of online risks: implications for policy and research. If you can’t access this, please e-mail me for a copy.

Victoria Nash is a Policy and Research Fellow at the Oxford Internet Institute (OII), responsible for connecting OII research with policy and practice. Her own particular research interests draw on her background as a political theorist, and concern the theoretical and practical application of fundamental liberal values in the Internet era. Recent projects have included efforts to map the legal and regulatory trends shaping freedom of expression online for UNESCO, analysis of age verification as a tool to protect and empower children online, and the role of information and Internet access in the development of moral autonomy.

Individuals often have little knowledge about the actual risks, and single instances of breaches are exaggerated in the media. Key to successful adoption of Internet-based health records is, however, how much a patient places trust in the technology: trust that data will be properly secured from inadvertent leakage, and trust that it will not be accessed by unauthorised strangers.

Situated in this context, my own research has taken a closer look at the structural and institutional factors influencing patient trust in Internet-based health records. Utilising a survey and interviews, the research has looked specifically at Germany – a very suitable environment for this question given its wide range of actors in the health system, and often being referred to as a “hard-line privacy country”. Germany has struggled for years with the introduction of smart cards linked to centralised Electronic Health Records, not only changing its design features over several iterations, but also battling negative press coverage about data security.

The first element to this question of patient trust is the “who”: that is, does it make a difference whether the health record is maintained by either a medical or a non-medical entity, and whether the entity is public or private? I found that patients clearly expressed a higher trust in medical operators, evidence of a certain “halo effect” surrounding medical professionals and organisations driven by patient faith in their good intentions. This overrode the concern that medical operators might be less adept at securing the data than (for example) most non-medical IT firms. The distinction between public and private operators is much more blurry in patients’ perception. However, there was a sense among the interviewees that a stronger concern about misuse was related to a preference for public entities who would “not intentionally give data to others”, while data theft concerns resulted in a preference for private operators – as opposed to public institutions who might just “shrug their shoulders and finger-point at subordinate levels”.

Equally important to the question of “who” is managing the data may be the “how”: that is, is the patient’s ability to access and control their health-record content perceived as trust enhancing? While the general finding of this research is that having the opportunity to both access and control their records helps to build patient trust, an often overlooked (and discomforting) factor is that easy access for the patient may also mean easy access for the rest of the family. In the words of one interviewee: “For example, you have Alzheimer’s disease or dementia. You don’t want everyone around you to know. They will say ‘show us your health record online’, and then talk to doctors about you – just going over your head.” Nevertheless, for most people I surveyed, having access and control of records was perceived as trust enhancing.

At the same time, a striking survey finding is how greater access and control of records can be less trust-enhancing for those with lower Internet experience, confidence, and breadth of use: as one older interviewee put it – “I am sceptical because I am not good at these Internet things. My husband can help me, but somehow it is not really worth this effort.” The quote reveals one of the facets of digital divides, and additionally highlights the relevance of life-stage in the discussion. Older participants see the benefits of sharing data (if it means avoiding unnecessary repetition of routine examinations) and are less concerned about outsider access, while younger people are more apprehensive of the risk of medical data falling into the wrong hands. An older participant summarised this very effectively: “If I was 30 years younger and at the beginning of my professional career or my family life, it would be causing more concern for me than now”. Finally, this reinforces the importance of legal regulations and security audits ensuring a general level of protection – even if the patient chooses not to be (or cannot be) directly involved in the management of their data.

Interestingly, the research also uncovered what is known as the certainty trough: not only are those with low online affinity highly suspicious of Internet-based health records – the experts are as well! The more different activities a user engaged in, the higher the suspicion of Internet-based health records. This confirms the notion that with more knowledge and more intense engagement with the Internet, we tend to become more aware of the risks – and lose trust in the technology and what the protections might actually be worth.

Finally, it is clear that the “who” and the “how” are interrelated, as a low degree of trust goes hand in hand with a desire for control. For a generally less trustworthy operator, access to records is not sufficient to inspire patient trust. While access improves knowledge and may allow for legal steps to change what is stored online, few people make use of this possibility; only direct control of what is stored online helps to compensate for a general suspicion about the operator. It is noteworthy here that there is a discrepancy between how much importance people place on having control, and how much they actually use it, but in the end, trust is a subjective concept that doesn’t necessarily reflect actual privacy and security.

The results of this research provide valuable insights for the further development of Internet-based health records. In short: to gain patient trust, the operator should ideally be of a medical nature and should allow the patients to get involved in how their health records are maintained. Moreover, policy initiatives designed to increase the Internet and health literacy of the public are crucial in reaching all parts of the population, as is an underlying legal and regulatory framework within which any Internet-based health record should be embedded.

Read the full paper: Rauer, Ulrike (2012) Patient Trust in Internet-based Health Records: An Analysis Across Operator Types and Levels of Patient Involvement in Germany. Policy and Internet 4 (2).

boyd / Hargittai: The public has many myths about young people’s use of technology. This is often perpetuated by media coverage that focuses on the extremes. Salacious negative headlines often capture people’s attention, even if the practices or incidents described are outliers and do not represent the majority’s experiences. While focusing on extremely negative and horrific incidents is a great way to attract attention and get readers, it does a disservice to young people, their parents, and ultimately society as a whole.

As researchers, we believe that it’s important to understand the nuances of what people experience when they engage with technology. Thus, we are interested in gaining a better understanding of their everyday practices — both the good and the bad. Our goal is to introduce research that can help contextualize socio-technical practices and provide insight into the diversity of viewpoints and perspectives that shape young people’s use of technology.

Ed: Your paper suggests we need a more granular understanding of how parental concerns relating to the Internet can vary across different groups. Why is this important? What are the main policy implications of this research?

boyd / Hargittai: Parents are often seen as the target of policy interventions. Many lawmakers imagine that they’re designing laws to help empower parents, but when you ask them to explain which parents they are empowering, it becomes clear that there’s an imagined parent that is not always representative of the diverse views and perspectives of all parents. We’re not opposed to laws that enable parents to protect their children, but we’re concerned whenever a class of people, especially a class as large as “parents,” is viewed as homogenous.

Parents have different and often conflicting views about what’s best for their children or for children writ large. This creates a significant challenge for designing interventions that are meant to be beneficial and applicable to a diverse group of people. What’s beneficial or desirable to one may not be positively received by another. More importantly, what’s helpful to one group of parents may not actually benefit parents or youth as a whole. As a result, we think it’s important to start interrogating assumptions that underpin technology policy interventions so that policymakers have a better understanding of how their decisions affect whom they’re hoping to reach.

Ed: What did your study reveal, and in particular, where do you see the greatest differences in attitudes arising? Did it reveal anything unexpected?

boyd / Hargittai: The most significant take-away from our research is that there are significant demographic differences in concerns about young people. Some of the differences are not particularly surprising. For example, parents of children who have been exposed to pornography or violent content, or who have bullied or been bullied, have greater concern that this will happen to their child. Yet, other factors may be more surprising. For example, we found significant racial and ethnic differences in how parents approach these topics. Black, Hispanic, and Asian parents are much more concerned about at least some of the online safety measures than Whites, even when controlling for socioeconomic factors and previous experiences.

While differences in cultural experiences may help explain some of these findings, our results raise serious questions as to the underlying processes and reasons for these discrepancies. Are these parents more concerned because they have a higher level of distrust for technology? Because they feel as though there are fewer societal protections for their children? Because they feel less empowered as parents? We don’t know. Still, our findings challenge policy-makers to think about the diversity of perspectives their law-making should address. And when they enact laws, they should be attentive to how those interventions are received. Just because parents of colour are more concerned does not mean that an intervention intended to empower them will do so. Like many other research projects, this study results in as many — if not more — questions than it answers.

Ed: Are parents worrying about the right things? For example, you point out that ‘stranger danger’ registers the highest level of concern from most parents, yet this is a relatively rare occurrence. Bullying is much more common, yet not such a source of concern. Do we need to do more to educate parents about risks, opportunities and coping?

boyd / Hargittai: Parental fear is a contested issue among scholars and for good reason. In many ways, it’s a philosophical issue. Should parents worry more about frequent but low-consequence issues? Or should they concern themselves more with the possibility of rare but devastating incidents? How much fear is too much fear? Fear is an understandable response to danger, but left unchecked, it can become an irrational response to perceived but unlikely risks. Fear can prevent injury, but too much fear can result in a form of protectionism that itself can be harmful. Most parents want to protect their children from harm but few think about the consequences of smothering their children in their efforts to keep them safe. All too often, in erring on the side of caution, we escalate a societal tendency to become overprotective, limiting our children’s opportunities to explore, learn, be creative and mature. Finding the right balance is very tricky.

People tend to fear things that they don’t understand. New technologies are often terrifying because they are foreign. And so parents are reasonably concerned when they see their children using tools that confound them. One of the best antidotes to fear is knowledge. Although this is outside of the scope of this paper, we strongly recommend that parents take the time to learn about the tools that their children are using, ideally by discussing them with their children. The more that parents can understand the technological choices and decisions made by their children, the more that parents can help them navigate the risks and challenges that they do face, online and off.

Ed: On the whole, it seems that parents whose children have had negative experiences online are more likely to say they are concerned, which seems highly appropriate. But we also have evidence from other studies that many parents are unaware of such experiences, and also that children who are more vulnerable offline, may be more vulnerable online too. Is there anything in your research to suggest that certain groups of parents aren’t worrying enough?

boyd / Hargittai: As researchers, we regularly use different methodologies and different analytical angles to get at various research questions. Each approach has its strengths and weaknesses, insights and blind spots. In this project, we surveyed parents, which allows us to get at their perspective, but it limits our ability to understand what they do not know or will not admit. Over the course of our careers, we’ve also surveyed and interviewed numerous youth and young adults, parents and other adults who’ve worked with youth. In particular, danah has spent a lot of time working with at-risk youth who are especially vulnerable. Unfortunately, what she’s learned in the process — and what numerous survey studies have shown — is that those who are facing some of the most negative experiences do not necessarily have positive home life experiences. Many youth face parents who are absent, addicts, or abusive; these are the youth who are most likely to be physically, psychologically, or socially harmed, online and offline.

In this study, we took parents at face value, assuming that parents are good actors with positive intentions. It is important to recognise, however, that this cannot be taken for granted. As with all studies, our findings are limited because of the methodological approach we took. We have no way of knowing whether or not these parents are paying attention, let alone whether or not their relationship to their children is unhealthy.

Although the issues of abuse and neglect are outside of the scope of this particular paper, these have significant policy implications. Empowering well-intended parents is generally a good thing, but empowering abusive parents can create unintended consequences for youth. This is an area where much more research is needed because it’s important to understand when and how empowering parents can actually put youth at risk in different ways.

Ed: What gaps remain in our understanding of parental attitudes towards online risks?

boyd / Hargittai: As noted above, our paper assumes well-intentioned parenting on behalf of caretakers. A study could explore online attitudes in the context of more information about people’s general parenting practices. Regarding our findings about attitudinal differences by race and ethnicity, much remains to be done. While existing literature alludes to some reasons as to why we might observe these variations, it would be helpful to see additional research aiming to uncover the sources of these discrepancies. It would be fruitful to gain a better understanding of what influences parental attitudes about children’s use of technology in the first place. What role do mainstream media, parents’ own experiences with technology, their personal networks, and other factors play in this process?

Another line of inquiry could explore how parental concerns influence rules aimed at children about technology uses and how such rules affect youth adoption and use of digital media. The latter is a question that Eszter is addressing in a forthcoming paper with Sabrina Connell, although that study does not include data on parental attitudes, only rules. Including details about parental concerns in future studies would allow more nuanced investigation of the above questions. Finally, much is needed to understand the impact that policy interventions in this space have on parents, youth, and communities. Even the most well-intentioned policy may inadvertently cause harm. It is important that all policy interventions are monitored and assessed as to both their efficacy and secondary effects.

Read the full paper: boyd, d., and Hargittai, E. (2013) Connected and Concerned: Exploring Variation in Parental Concerns About Online Safety Issues. Policy and Internet 5 (3).

danah boyd and Eszter Hargittai were talking to blog editor David Sutcliffe.

Ed: Could you briefly outline your study?

Patty: We investigated the role of Twitter during the 2009 swine flu pandemics from two perspectives. Firstly, we demonstrated the role of the social network to detect an upcoming spike in an epidemic before the official surveillance systems – up to week in the UK and up to 2-3 weeks in the US – by investigating users who “self-diagnosed” themselves posting tweets such as “I have flu / swine flu”. Secondly, we illustrated how online resources reporting the WHO declaration of “pandemics” on 11 June 2009 were propagated through Twitter during the 24 hours after the official announcement [1,2,3].

Ed: Disease control agencies already routinely follow media sources; are public health agencies  aware of social media as another valuable source of information?

Patty:  Social media are providing an invaluable real-time data signal complementing well-established epidemic intelligence (EI) systems monitoring online media, such as MedISys and GPHIN. While traditional surveillance systems will remain the pillars of public health, online media monitoring has added an important early-warning function, with social media bringing  additional benefits to epidemic intelligence: virtually real-time information available in the public domain that is contributed by users themselves, thus not relying on the editorial policies of media agencies.

Public health agencies (such as the European Centre for Disease Prevention and Control) are interested in social media early warning systems, but more research is required to develop robust social media monitoring solutions that are ready to be integrated with agencies’ EI services.

Ed: How difficult is this data to process? Eg: is this a full sample, processed in real-time?

Patty:  No, obtaining all Twitter search query results is not possible. In our 2009 pilot study we were accessing data from Twitter using a search API interface querying the database every minute (the number of results was limited to 100 tweets). Currently, only 1% of the ‘Firehose’ (massive real-time stream of all public tweets) is made available using the streaming API. The searches have to be performed in real-time as historical Twitter data are normally available only through paid services. Twitter analytics methods are diverse; in our study, we used frequency calculations, developed algorithms for geo-location, automatic spam and duplication detection, and applied time series and cross-correlation with surveillance data [1,2,3].

Ed: What’s the relationship between traditional and social media in terms of diffusion of health information? Do you have a sense that one may be driving the other?

Patty: This is a fundamental question. “Does media coverage of certain topic causes buzz on social media or does social media discussion causes media frenzy?” This was particularly important to investigate for the 2009 swine flu pandemic, which experienced unprecedented media interest. While it could be assumed that disease cases preceded media coverage, or that media discussion sparked public interest causing Twitter debate, neither proved to be the case in our experiment. On some days, media coverage for flu was higher, and on others Twitter discussion was higher; but peaks seemed synchronized – happening on the same days.

Ed: In terms of communicating accurate information, does the Internet make the job easier or more difficult for health authorities?

Patty: The communication of risk in any public health emergencies is a complex task for government and healthcare agencies; this task is made more challenging when citizens are bombarded with online information, from a variety of sources that vary in accuracy. This has become even more challenging with the increase in users accessing health-related information on their mobile phones (17% in 2010 and 31% in 2012, according to the US Pew Internet study).

Our findings from analyzing Twitter reaction to online media coverage of the WHO declaration of swine flu as a “pandemic” (stage 6) on 11 June 2009, which unquestionably was the most media-covered event during the 2009 epidemic, indicated that Twitter does favour reputable sources (such as the BBC, which was by far the most popular) but also that bogus information can still leak into the network.

Ed: What differences do you see between traditional and social media, in terms of eg bias / error rate of public health-related information?

Patty: Fully understanding quality of media coverage of health topics such as the 2009 swine flu pandemics in terms of bias and medical accuracy would require a qualitative study (for example, one conducted by Duncan in the EU [4]). However, the main role of social media, in particular Twitter due to the 140 character limit, is to disseminate media coverage by propagating links rather than creating primary health information about a particular event. In our study around 65% of tweets analysed contained a link.

Ed: Google flu trends (which monitors user search terms to estimate worldwide flu activity) has been around a couple of years: where is that going? And how useful is it?

Patty: Search companies such as Google have demonstrated that online search queries for keywords relating to flu and its symptoms can serve as a proxy for the number of individuals who are sick (Google Flu Trends), however, in 2013 the system “drastically overestimated peak flu levels”, as reported by Nature. Most importantly, however, unlike Twitter, Google search queries remain proprietary and are therefore not useful for research or the construction of non-commercial applications.

Ed: What are implications of social media monitoring for countries that may want to suppress information about potential pandemics?

Patty: The importance of event-based surveillance and monitoring social media for epidemic intelligence is of particular importance in countries with sub-optimal surveillance systems and those lacking the capacity for outbreak preparedness and response. Secondly, the role of user-generated information on social media is also of particular importance in counties with limited freedom of press or those that actively try to suppress information about potential outbreaks.

Ed: Would it be possible with this data to follow spread geographically, ie from point sources, or is population movement too complex to allow this sort of modelling?

Patty: Spatio-temporal modelling is technically possible as tweets are time-stamped and there is a support for geo-tagging. However, the location of all tweets can’t be precisely identified; however, early warning systems will improve in accuracy as geo-tagging of user generated content becomes widespread. Mathematical modelling of the spread of diseases and population movements are very topical research challenges (undertaken by, for example, by Colliza et al. [5]) but modelling social media user behaviour during health emergencies to provide a robust baseline for early disease detection remains a challenge.

Ed: A strength of monitoring social media is that it follows what people do already (eg search / Tweet / update statuses). Are there any mobile / SNS apps to support collection of epidemic health data? eg a sort of ‘how are you feeling now’ app?

Patty: The strength of early warning systems using social media is exactly in the ability to piggy-back on existing users’ behaviour rather than having to recruit participants. However, there are a growing number of participatory surveillance systems that ask users to provide their symptoms (web-based such as Flusurvey in the UK, and “Flu Near You” in the US that also exists as a mobile app). While interest in self-reporting systems is growing, challenges include their reliability, user recruitment and long-term retention, and integration with public health services; these remain open research questions for the future. There is also a potential for public health services to use social media two-ways – by providing information over the networks rather than only collect user-generated content. Social media could be used for providing evidence-based advice and personalized health information directly to affected citizens where they need it and when they need it, thus effectively engaging them in active management of their health.

References

[1.] M Szomszor, P Kostkova, C St Louis: Twitter Informatics: Tracking and Understanding Public Reaction during the 2009 Swine Flu Pandemics, IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology 2011, WI-IAT, Vol. 1, pp.320-323.

[2.]  Szomszor, M., Kostkova, P., de Quincey, E. (2010). #swineflu: Twitter Predicts Swine Flu Outbreak in 2009. M Szomszor, P Kostkova (Eds.): ehealth 2010, Springer Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering LNICST 69, pages 18-26, 2011.

[3.] Ed de Quincey, Patty Kostkova Early Warning and Outbreak Detection Using Social Networking Websites: the Potential of Twitter, P Kostkova (Ed.): ehealth 2009, Springer Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering LNICST 27, pages 21-24, 2010.

[4.] B Duncan. How the Media reported the first day of the pandemic H1N1) 2009: Results of EU-wide Media Analysis. Eurosurveillance, Vol 14, Issue 30, July 2009

[5.] Colizza V, Barrat A, Barthelemy M, Valleron AJ, Vespignani A (2007) Modeling the worldwide spread of pandemic influenza: Baseline case an containment interventions. PloS Med 4(1): e13. doi:10.1371/journal. pmed.0040013

Further information on this project and related activities, can be found at: BMJ-funded scientific film: http://www.youtube.com/watch?v=_JNogEk-pnM ; Can Twitter predict disease outbreaks? http://www.bmj.com/content/344/bmj.e2353 ; 1st International Workshop on Public Health in the Digital Age: Social Media, Crowdsourcing and Participatory Systems (PHDA 2013): http://www.digitalhealth.ws/ ; Social networks and big data meet public health @ WWW 2013: http://www2013.org/2013/04/25/social-networks-and-big-data-meet-public-health/

Patty Kostkova was talking to blog editor David Sutcliffe.

Dr Patty Kostkova is a Principal Research Associate in eHealth at the Department of Computer Science, University College London (UCL) and held a Research Scientist post at the ISI Foundation in Italy. Until 2012, she was the Head of the City eHealth Research Centre (CeRC) at City University, London, a thriving multidisciplinary research centre with expertise in computer science, information science and public health. In recent years, she was appointed a consultant at WHO responsible for the design and development of information systems for international surveillance.

Researchers who were instrumental in this project include Ed de Quincey, Martin Szomszor and Connie St Louis.

Ed: European legislation introduced in 2011 requires Member States to ensure the prompt removal of child pornography websites hosted in their territory and to endeavour to obtain the removal of such websites hosted outside; leaving open the option to block access by users within their own territory. What is problematic about this blocking?

Authors: From a technical point of view, all possible blocking methods that could be used by Member States are ineffective as they can all be circumvented very easily. The use of widely available technologies (like encryption or proxy servers) or tiny changes in computer configurations (for instance the choice of DNS-server), that may also be used for better performance or the enhancement of security or privacy, enable circumvention of blocking methods. Another problem arises from the fact that this legislation only targets website content while offenders often use other technologies such as peer-to-peer systems, newsgroups or email.

Ed: Many of these blocking activities stem from European efforts to combat child pornography, but you suggest that child protection may be used as a way to add other types of content to lists of blocked sites – notably those that purportedly violate copyright. Can you explain how this “mission creep” is occurring, and what the risks are?

Authors: Combating child pornography and child abuse is a universal and legitimate concern. With regard to this subject there is a worldwide consensus that action must be undertaken in order to punish abusers and protect children. Blocking measures are usually advocated on the basis of the argument that access to these images must be prevented, hence avoiding that users stumble upon child pornography inadvertently. Whereas this seems reasonable with regard to this particular type of content, in some countries governments increasingly use blocking mechanisms for other ‘illegal’ content, such as gambling or copyright-infringing content, often in a very non-transparent way, without clear or established procedures.

It is, in our view, especially important at a time when governments do not hesitate to carry out secret online surveillance of citizens without any transparency or accountability, that any interference with online content must be clearly prescribed by law, have a legitimate aim and, most importantly, be proportional and not go beyond what is necessary to achieve that aim. In addition, the role of private actors, such as ISPs, search engine companies or social networks, must be very carefully considered. It must be clear that decisions about which content or behaviours are illegal and/or harmful must be taken or at least be surveyed by the judicial power in a democratic society.

Ed: You suggest that removal of websites at their source (mostly in the US and Canada) is a more effective means of stopping the distribution of child pornography — but that European law enforcement has often been insufficiently committed to such action. Why is this? And how easy are cross-jurisdictional efforts to tackle this sort of content?

Authors: The blocking of websites is, although questionably ineffective as a method of making the content inaccessible, a quick way to be seen to take action against the appearance of unwanted material on the Internet. The removal of content on the other hand requires not only the identification of those responsible for hosting the content but more importantly the actual perpetrators. This is of course a more intrusive and lengthy process, for which law enforcement agencies currently lack resources.

Moreover, these agencies may indeed run into obstacles related to territorial jurisdiction and difficult international cooperation. However, prioritising and investing in actual removal of content, even though not feasible in certain circumstances, will ensure that child sexual abuse images do not further circulate, and, hence, that the risk of repetitive re-victimization of abused children is reduced.

Read the full paper: Karel Demeyer, Eva Lievens and Jos Dumortier (2012) Blocking and Removing Illegal Child Sexual Content: Analysis from a Technical and Legal Perspective. Policy and Internet 4 (3-4).

Karel Demeyer, Eva Lievens and Jos Dumortier were talking to blog editor Heather Ford.

Despite large investments of law enforcement resources, online child exploitation is nowhere near under control, and while there are numerous technological products to aid in finding child pornography online, they still require substantial human intervention. Despite this, steps can be taken to increase the automation process of these searches, to reduce the amount of content police officers have to examine, and increase the time they can spend on investigating individuals.

While law enforcement agencies will aim for maximum disruption of online child exploitation networks by targeting the most connected players, there is a general lack of research on the structural nature of these networks; something we aimed to address in our study, by developing a method to extract child exploitation networks, map their structure, and analyze their content. Our custom-written Child Exploitation Network Extractor (CENE) automatically crawls the Web from a user-specified seed page, collecting information about the pages it visits by recursively following the links out of the page; the result of the crawl is a network structure containing information about the content of the websites, and the linkages between them [2].

We chose ten websites as starting points for the crawls; four were selected from a list of known child pornography websites while the other six were selected and verified through Google searches using child pornography search terms. To guide the network extraction process we defined a set of 63 keywords, which included words commonly used by the Royal Canadian Mounted Police to find illegal content; most of them code words used by pedophiles. Websites included in the analysis had to contain at least seven of the 63 unique keywords, on a given web page; manual verification showed us that seven keywords distinguished well between child exploitation web pages and regular web pages. Ten sports networks were analyzed as a control.

The web crawler was found to be able to properly identify child exploitation websites, with a clear difference found in the hardcore content hosted by child exploitation and non-child exploitation websites. Our results further suggest that a ‘network capital’ measure — which takes into account network connectivity, as well as severity of content — could aid in identifying the key players within online child exploitation networks. These websites are the main concern of law enforcement agencies, making the web crawler a time saving tool in target prioritization exercises. Interestingly, while one might assume that website owners would find ways to avoid detection by a web crawler of the type we have used, these websites — despite the fact that much of the content is illegal — turned out to be easy to find. This fits with previous research that has found that only 20-25 percent of online child pornography arrestees used sophisticated tools for hiding illegal content [3,4].

As mentioned earlier, the huge amount of content found on the Internet means that the likelihood of eradicating the problem of online child exploitation is nil. As the decentralized nature of the Internet makes combating child exploitation difficult, it becomes more important to introduce new methods to address this. Social network analysis measurements, in general, can be of great assistance to law enforcement investigating all forms of online crime—including online child exploitation. By creating a web crawler that reduces the amount of hours officers need to spend examining possible child pornography websites, and determining whom to target, we believe that we have touched on a method to maximize the current efforts by law enforcement. An automated process has the added benefit of aiding to keep officers in the department longer, as they would not be subjugated to as much traumatic content.

There are still areas for further research; the first step being to further refine the web crawler. Despite being a considerable improvement over a manual analysis of 300,000 web pages, it could be improved to allow for efficient analysis of larger networks, bringing us closer to the true size of the full online child exploitation network, but also, we expect, to some of the more hidden (e.g., password/membership protected) websites. This does not negate the value of researching publicly accessible websites, given that they may be used as starting locations for most individuals.

Much of the law enforcement to date has focused on investigating images, with the primary reason being that databases of hash values (used to authenticate the content) exists for images, and not for videos. Our web crawler did not distinguish between the image content, but utilizing known hash values would help improve the validity of our severity measurement. Although it would be naïve to suggest that online child exploitation can be completely eradicated, the sorts of social network analysis methods described in our study provide a means of understanding the structure (and therefore key vulnerabilities) of online networks; in turn, greatly improving the effectiveness of law enforcement.

[1] Engeler, E. 2009. September 16. UN Expert: Child Porn on Internet Increases. The Associated Press.

[2] Westlake, B.G., Bouchard, M., and Frank, R. 2012. Finding the Key Players in Online Child Exploitation Networks. Policy and Internet 3 (2).

[3] Carr, J. 2004. Child Abuse, Child Pornography and the Internet. London: NCH.

[4] Wolak, J., D. Finkelhor, and K.J. Mitchell. 2005. “Child Pornography Possessors Arrested in Internet-Related Crimes: Findings from the National Juvenile Online Victimization Study (NCMEC 06–05–023).” Alexandria, VA: National Center for Missing and Exploited Children.

Read the full paper: Westlake, B.G., Bouchard, M., and Frank, R. 2012. Finding the Key Players in Online Child Exploitation Networks. Policy and Internet 3 (2).

While the study reflects a high level of parental anxiety about the potential of social networking sites to distract their offspring, and shows that some parents despair at their children’s tendency to multitask on mobile devices, the research by Oxford University’s Department of Education and Oxford Internet Institute concludes that there are substantial educational advantages in teenagers being able to access the internet at home.

Teenagers who do not have access to the internet in their home have a strong sense of being ‘educationally disadvantaged’, warns the study. At the time of the study, the researchers estimated that around 10 per cent of the teenagers were without online connectivity at home, with most of this group living in poorer households. While recent figures from the Office of National Statistics suggest this dropped to five per cent in 2012, the researchers say that still leaves around 300,000 children without internet access in their homes.

The researchers’ interviews with teenagers reveal that they felt shut out of their peer group socially and also disadvantaged in their studies as so much of the college or school work set for them to do at home required online research or preparation. One teenager, whose parents had separated, explained that he would ring his father who had internet access and any requested materials were then mailed to him through the post.

Researcher Dr Rebecca Eynon commented: ‘While it’s difficult to state a precise figure for teenagers without access to the internet at home, the fact remains that in the UK, there is something like 300,000 young people who do not – and that’s a significant number. Behind the statistics, our qualitative research shows that these disconnected young people are clearly missing out both educationally and socially.’

In an interview with a researcher, one 14-year old boy said: ‘We get coursework now in Year 9 to see what groups we’re going to go in Year 10. And people with internet, they can get higher marks because they can like research on the internet … my friends are probably on it [MSN] all the day every day. And like they talk about it in school, what happened on MSN.’

Another teenager, aged 15, commented: ‘It was bell gone and I have a lot of things that I could write and I was angry that I haven’t got a computer because I might finish it at home when I’ve got lots of time to do it. But because when I’m at school I need to do it very fast.’

Strikingly, this study contradicts claims that others have made about the potential risks of such technologies adversely affecting the ability of teenagers to concentrate on serious study. The researchers, Dr Chris Davies and Dr Rebecca Eynon, found no evidence to support this claim. Furthermore, their study concludes that the internet has opened up far more opportunities for young people to do their learning at home.

Dr Davies said: ‘Parental anxiety about how teenagers might use the very technologies that they have bought their own children at considerable expense is leading some to discourage their children from becoming confident users. The evidence, based on the survey and hundreds of interviews, shows that parents have tended to focus on the negative side – especially the distracting effects of social networking sites – without always seeing the positive use that their children often make of being online.’

Teenagers’ experiences of the social networking site Facebook appear to be mixed, says the study. Although some regarded Facebook as an integral part of their social life, others were concerned about the number of arguments that had escalated due to others wading in as a result of comments and photographs being posted.

The age of teenagers using Facebook for the first time was found to go down over the three year period from around 16 years old in 2008 to 12 or 13 years old by 2011. Interviews reveal that even the very youngest teenagers who were not particularly interested felt under some peer pressure to join. But the study also suggests that the popularity of Facebook is waning, with teenagers now exploring other forms of social networking.

Dr Davies commented: ‘There is no steady state of teenage technology use – fashions and trends are constantly shifting, and things change very rapidly when they do change.’

The research was part funded by Becta, the British Educational Communications and Technology Agency, a non-departmental public body formed under the last Labour government. The study findings are contained in a new book entitled, Teenagers and Technology, published by Routledge in November 2012.

Emergency contraception has long been heralded as a “second chance” for women to prevent pregnancy after unprotected intercourse. However, the commercial promotion and use of ECPs has been a highly contentious issue in the United States, a fact that has had a significant impact on legislative action and accessibility. Due to their limited window of effectiveness and given that people do not tend to obtain them until the moment when they are needed urgently, it is essential for people to be able to find accurate information about ECPs as quickly as possible.

Our study investigated empirically how over 200 young college students (18-19 years old) at two college campuses in the Midwestern United States searched for and evaluated information about emergency contraception. They were given the hypothetical scenario: “You are at home in the middle of summer. A friend calls you frantically on a Friday at midnight. The condom broke while she was with her boyfriend. What can she do to prevent pregnancy? Remember, neither of you is on campus. She lives in South Bend, Indiana.” All of the students had considerable experience with using the Internet.

Worryingly, a third of the participants, after looking for information online, were unable to conclude that the friend should seek out ECPs. Less than half gave what we consider the ideal response: to have the friend purchase ECPs over the counter at a pharmacy. Some participants suggested such solutions as “wait it out,” “adoption,” “visit a gynecologist” (in the incorrect location), and purchasing another condom. Three percent of respondents came to no conclusion at all.

While adolescents often claim to be confident in searching for information online, they are often unsystematic in their search and few students made a concerted effort to verify information they found during their search. The presence of a dot-org domain name was sometimes cited as a measure of credibility: “Cause it’s like a government issued kind of website,” noted one participant. While it’s encouraging that students are aware of different top-level domain names, it’s alarming that their knowledge of what they signify can be wrong: dot-org sites are not sanctioned any more than are dot-com sites and thus should not be considered a signal of credibility.

Another student assumed that “the main website” for the morning after pill was morningafterpill.org, which happens to be sponsored by the American Life League, a pro-life organization. The website includes articles with titles such as “Emergency Contraception: the Truth, the Whole Truth, and Nothing but the Truth,” as well as advocacy by medical professionals matching the perspectives of the American Life League. This demonstrates the way in which people and organizations with a particular agenda can publicize any type of information – in this case erroneous health information – to the public.

Overall, the findings suggest that despite information theoretically available on the Web about emergency contraception, even young adults with considerable online experiences may not be able to find it in a time of need. Many respondents were uncertain of how to begin looking for information; some did not immediately consider the Internet as a primary source for it. An important policy implication of this study is that it is problematic to assume that just because content exists online, it is easily within the reach of all users. In particular, it is a mistake to think that just because young people grew up with digital media, they are universally savvy with finding and evaluating Web content.

Given the importance of finding credible and accurate health-related content, it is important to understand the strategies people use to find information so that obstacles can be addressed – rather than taking such know-how for granted, educational institutions should think about incorporating related content into their curricula. Additionally, related services should be available at establishments such as public libraries available to those not enrolled in school.

In some cases particular search terms determined whether people found the right information: providers of content about emergency contraception need to be aware of this. The study also raises questions about search engine practices. While search engine companies seem to take pride in letting their algorithms sort out the ranking of search results, is it ideal or responsible to leave content important to people’s health in the hands of automated processes that are open to manipulation?

Algorithms themselves are not neutral – they include lots of decisions taken by their creators – yet the idea of “algorithm literacy” is not a topic taken up in educational curricula or public conversations.

However, the reality is far more nuanced and complex than this — when we empirically explore how young people really engage with the Internet and related technology we see a significant amount of diversity in how and why they use it, and the influences it has on their lives. We know from nationally representative survey data that around 10% of young people in the UK (aged 17–23) define themselves as people who no longer use the Internet, that is as ‘lapsed users’. This group is fascinating. Why do these people stop using the Internet given its prevalence and value in the lives of the majority of their peers? What difficulties do they face in being unable to connect properly with the online world?

The widely held and very powerful assumption by government, commercial organizations and the wider public that all young people are frequent and confident users of the Internet is clearly inaccurate. Worryingly, however, this public assumption that the current generation of youth is ‘born digital’ is so powerful that it has informed numerous policies and initiatives that determine young people’s lives. Furthermore, the majority of academic research investigating how young people access, use and experience the Internet actually focuses on those we might consider as belonging to the digital mainstream, with a relatively limited focus on those who do not use the Internet, or who use it in very limited ways. This is primarily because most of the work in this area is based on large scale surveys (which fail to pick up detail on minority groups) and because existing qualitative studies tend to focus on moderate to high-end Internet users, who are more willing to participate in academic studies; and who are also far easier to find and recruit.

A recent study undertaken by myself and Anne Geniets for the Nominet Trust examined why these young people are disconnected, how they think and feel about this disconnection, and the extent to which this is due to reasons of exclusion or choice. We also examined the implications for their daily lives and considered how the experiences of these young people could inform the UK’s digital inclusion strategy. Thirty-six in–depth interviews were undertaken with young people (aged 17-23) who considered themselves to be infrequent or lapsed Internet users.

We uncovered a complex set of reasons why these people are not online, including fear of bullying, literacy issues, poverty, lack of skills to use the Internet, and lack of access. However, we also found that the respondents generally recognised the huge importance of the Internet and the tangible benefits it brought to their (online) peers. Some were frustrated that they couldn’t join in; others were resigned about it. Only a few didn’t see the point of the Internet. When so much of today’s world is premised on effective use of the Internet to (for example) drive the economy and employment, this is worrying; particularly given the gap for these young people is only going to widen in the future. As more and more services both in and outside the public sector go ‘digital by default’, for example, when supermarkets only accept online applications, the relative disadvantage for this group increases.

It is therefore important to recognise that while the UK Government’s ‘digital by default’ strategy may be successful at encouraging the unwilling (but capable) online – such as many older users – it may not be appropriate for this group, who tend to be high users of government services but who for various reasons – cognitive, psychological, socio-cultural, physical and material – are still offline, despite recognising the Internet’s importance in the world around them. Perhaps most worrying (and something that was mentioned by many of our respondents) was that being young and therefore supposedly ‘digital’ – according to today’s societal norms – actually made it much harder for them to seek help. Simply recognising the fact that not all young people are digitally literate or active is important for the people who interact with them: for example teachers, potential employers, social workers, government employees, and job centre staff. We need to allow for the possibility that young people may need support in using the Internet, enable them to identify problems with their skill sets, and move forward with educational initiatives to ensure that all young people have an opportunity to fully explore the online world and develop the skills needed to support that process while they are still in education.

There were many surprises in this chronically under-researched group, particularly in what they understood by ‘Internet use’. Some defined themselves as non-users but still occasionally used email; others gave their email passwords to a friend to handle their accounts (which including writing emails) for them. This ambiguity surrounding user definitions of ‘use’ and ‘non-use’ should be recognised in future research on this topic; the concept of ‘meaningful use’ of the Internet should also be explored. In some ways this ambiguity offers something of a positive message: while these young people are well aware of their difference in relation to their peers, and do not consider themselves to be ‘proper’ Internet users, they are still sometimes able to access and use the Internet — if to a very limited extent. As this group is often willing to try to use the Internet, seeing it as a normal and necessary part of life, we believe that successful intervention is possible.

However, it was clear from the interviews that many of these young people where experiencing difficult situations — including homelessness, unemployment, bullying and increasing isolation. Being excluded from the Internet’s benefits means that these young people are probably even more likely to belong to a social ‘out–group’; and as social psychological research has shown, this can have a significant negative effect on identity development and the perception of self. For young people who are already disadvantaged this is obviously less than ideal. We need to encourage initiatives that develop and extend social capital for these young people, perhaps by facilitating connections between those who used to be outside the digital mainstream and those who are still outside it.

A good start might be to simply acknowledge that this group actually exists, and to adopt a more nuanced understanding of what it means to actually use the Internet in a meaningful way.

Is this aim of achieving user empowerment realistic? In their examination of health queries submitted to the NHS Direct online enquiry service, John Powell and Sharon Boden find that while patient empowerment does occur in the use of online health services, it is constrained and context dependent. Policymakers wishing to promote greater choice and control among health system users should therefore take account of the limits to empowerment as well as barriers to participation. The Dutch government’s online public national health and care portal similarly aims to facilitate consumer decision-making behavior and increasing transparency and accountability to improve quality of care and functioning of health markets. Interestingly, Hans Ossebaard, Lisette van Gemert-Pijnen and Erwin Seydel find the influence of the Dutch portal on choice behavior, awareness, and empowerment of users to actually be small.

The Internet is often discussed in terms of empowering (or even endangering) patients through broadening of access to medical and health-related information, but there is evidence that concerns about serious negative effects of using the Internet for health information may be ill-founded. The cancer patients in the study by Alison Chapple, Julie Evans and Sue Ziebland gave few examples of harm from using the Internet or of damage caused to their relationships with health professionals. While policy makers have tended to focus on regulating the factual content of online information, in this study it was actually the consequences of stumbling on factually correct (but unwelcome) information that most concerned the patients and families; good practice guidelines for health information may therefore need to pay more attention to website design and user routing, as well as to the accuracy of content.

Policy makers and health professionals should also acknowledge the often highly individual strategies people use to access health information online, and understand how these practices are shaped by technology — the study by Astrid Mager found that the way people collected and evaluated online information about chronic diseases was shaped by search engines as much as by their individual medical preferences.

Many people still lack the necessary skills to navigate online content effectively. Eszter Hargittai and Heather Young examined the experiences a diverse group of young adults looking for information about emergency contraception online, finding that the majority of the study group could not identify the most efficient way of acquiring emergency contraception in a time of need. Given the increasing trend for people to turn to the Internet for health information, users must possess the necessary skills to make effective and efficient use of it; an important component of this may concern educational efforts to help people better navigate the Web. Improving general e-Health literacy is one of several recommendations by Maria De Jesus and Chenyang Xiao, who examined how Hispanic adults in the United States search for health information online. They report a striking language divide, with English proficiency of the user largely predicting online health information-seeking behavior.

Lastly, but no less importantly, is the policy challenge of addressing the issue of patient trust. The study by Ulrike Rauer on the structural and institutional factors that influence patient trust in Internet-based health records found that while patients typically considered medical operators to be more trustworthy than non-medical ones, there was no evidence of a “public–private” divide; patients perceived physicians and private health insurance providers to be more trustworthy than the government and corporations. Patient involvement in terms of access and control over their records was also found to be trust enhancing.

A lack of policy measures is a common barrier to success of eHealth initiatives; it is therefore essential that we develop measures that facilitate the adoption of initiatives and that demonstrate their success through improvement in services and the health status of the population. The articles presented in this special issue of Policy & Internet provide the sort of evidence-based insight that is urgently needed to help shape these policy measures. The empirical research and perspectives gathered here will make a valuable contribution to future efforts in this area.

The Government’s recent digital inclusion campaigns seem at first sight to recognise this need. For example, the UK ICT Strategy paper pledges that “The Government will work to make citizen-focused transactional services ‘digital by default’ where appropriate using Directgov as the single domain for citizens to access public services and government information. For those for whom digital channels are less accessible (for example, some older or disadvantaged people) the Government will enable a network of ‘assisted digital’ service providers, such as Post Offices, UK online centres and other local service providers” (§45, UK ICT Strategy 2011).

‘By default’ strategies are at the core of a concept called ‘libertarian paternalism’, which initially was advanced and popularized by two American academics, Richard Thaler and Cass Sunstein, and since has been adopted by a number of governments around the world. In the UK, it has inspired the creation of the Cabinet Office’s Behavioural Insight Team, commonly known in Whitehall as the ‘Nudge Unit’.

The idea behind the libertarian paternalism concept is that the government gently encourages citizens to act in socially beneficial ways, without infringing their freedom or liberty, and through these nudges it improves economic welfare and well being for the whole of society. Governments nudge by reorganizing the context in which citizens make certain decisions, a strategy also referred to as ‘choice architecture’. To quote a common example, it may not be at the forefront of learner drivers’ mind to sign up for the organ donor register, but by asking learner drivers whether they would like to join the register at the end of their application for a provisional driving licence, many learner drivers may choose to opt in. In other words, while the learner drivers are by default not enrolled as organ donors, they are gently ‘nudged’ by authorities to join the organ donors register and to help tackle the nationwide shortage of organ donations.

To apply libertarian paternalism to issues where citizens have the freedom to make a choice is sensible. Libertarian paternalism after all already has proven to be beneficial in a number of aspects of civic life. But by applying the concept to issues where citizens do not have a choice because of restricted resources, by default strategies risk becoming a tool for social exclusion. This poses a democratic problem.

This, our research suggests, is a current threat for young people who are high users of government services but infrequent users of the Internet.

The benefits of moving government services online are clear. Older citizens who do not go online often do not do so due to a range of factors, such as lack of skills, lack of interest or absence of an Internet connection. While these reasons are complex, there is often, at least to some extent, some element of a digital choice. Thus, for many people within this group, digital by default strategies that encourage citizens to use the government’s online services may work well. For example, through the provision of support at UK online centres and initiatives such asGo On Give an Hour in the context of the UK Race Online 2012 campaign.

However, for younger citizens, who have used the Internet at school and have grown up with the Internet as a part of normal life, not using the Internet or using the Internet in limited ways is more likely to be linked to issues such as the costs of going online. The majority of this group do not need to be nudged into using the Internet.

Preliminary findings of our ‘Lapsed Use of the Internet Amongst Young People in the UK’ project confirm this hypothesis. They suggest that particularly young people in transition often find it difficult to get access to the Internet. These are young people who just left school and don’t have Internet access at home, young people who are in transitory homes or homeless, young people who have just arrived in the UK as a refugee and young people who are working part-time only, or are unemployed and therefore cannot afford to access the Internet.

Sometimes the computers are full, so I go to the British library and can check my email and can see whether I have received something, because at the moment I am looking for jobs. If I am waiting for something important or if I have applied for a job … I have to keep checking my Internet and if I don’t have access to the Internet I really worry. [Alexandra, 20]

They actually cut the funding. And this is why places like the youth club here and Connexions that used to be open are no longer open, and the one-stop shop in L, all got their fundings cut, and they closed down. And, they, I’m surprised this place [youth club] is open, you know. But what can you do?  Nothing, you would have nothing. You would seriously have nothing… [Giorgio, 23]

Young people in transition are particularly at risk of being both socially and digitally excluded. Because of the restriction of their resources, accessing the Internet for them is not typically a matter of choice. This is why an ICT strategy based on choice architecture is not going to work for the majority of young people who are currently ex-users or non-users of the Internet. Instead, there is a danger that digital by default strategies doubly disadvantage those young people without Internet access, by aggravating and slowing down their enrolment process for government services and job programmes.

Therefore, strategies need to be developed that target young ex- and non-users of the Internet specifically, to ensure that these young people who are already part of an ‘Internet by default generation’ do not slip through the net, both technologically and socially.