In their Policy & Internet article “Understanding Popularity, Reputation, and Social Influence in the Twitter Society“, David Garcia, Pavlin Mavrodiev, Daniele Casati, and Frank Schweitzer examine popularity, reputation, and social influence on Twitter using network information on more than 40 million users. They integrate measurements of popularity, reputation, and social influence to evaluate what keeps users active, what makes them more popular, and what determines their influence in the network.

Popularity in the Twitter social network is often quantified as the number of followers of a user. That implies that it doesn’t matter why some user follows you, or how important she is, your popularity only measures the size of your audience. Reputation, on the other hand, is a more complicated concept associated with centrality. Being followed by a highly reputed user has a stronger effect on one’s reputation than being followed by someone with low reputation. Thus, the simple number of followers does not capture the recursive nature of reputation.

In their article, the authors examine the difference between popularity and reputation on the process of social influence. They find that there is a range of values in which the risk of a user becoming inactive grows with popularity and reputation. Popularity in Twitter resembles a proportional growth process that is faster in its strongly connected component, and that can be accelerated by reputation when users are already popular. They find that social influence on Twitter is mainly related to popularity rather than reputation, but that this growth of influence with popularity is sublinear. In sum, global network metrics are better predictors of inactivity and social influence, calling for analyses that go beyond local metrics like the number of followers.

We caught up with the authors to discuss their findings:

Ed.: Twitter is a convenient data source for political scientists, but they tend to get criticised for relying on something that represents only a tiny facet of political activity. But Twitter is presumably very useful as a way of uncovering more fundamental / generic patterns of networked human interaction?

David: Twitter as a data source to study human behaviour is both powerful and limited. Powerful because it allows us to quantify and analyze human behaviour at scales and resolutions that are simply impossible to reach with traditional methods, such as experiments or surveys. But also limited because not every aspect of human behaviour is captured by Twitter and using its data comes with significant methodological challenges, for example regarding sampling biases or platform changes. Our article is an example of an analysis of general patterns of popularity and influence that are captured by spreading information in Twitter, which only make sense beyond the limitations of Twitter when we frame the results with respect to theories that link our work to previous and future scientific knowledge in the social sciences.

Ed.: How often do theoretical models (i.e. describing the behaviour of a network in theory) get linked up with empirical studies (i.e. of a network like Twitter in practice) but also with qualitative studies of actual Twitter users? And is Twitter interesting enough in itself for anyone to attempt to develop an overall theoretico-empirico-qualitative theory about it?

David: The link between theoretical models and large-scale data analyses of social media is less frequent than we all wish. But the gap between disciplines seems to be narrowing in the last years, with more social scientists using online data sources and computer scientists referring better to theories and previous results in the social sciences. What seems to be quite undeveloped is an interface with qualitative methods, specially with large-scale analyses like ours.

Qualitative methods can provide what data science cannot: questions about important and relevant phenomena that then can be explained within a wider theory if validated against data. While this seems to me as a fertile ground for interdisciplinary research, I doubt that Twitter in particular should be the paragon of such combination of approaches. I advocate for starting research from the aspect of human behaviour that is the subject of study, and not from a particularly popular social media platform that happens to be used a lot today, but might not be the standard tomorrow.

Ed.: I guess I’ve see a lot of Twitter networks in my time, but not much in the way of directed networks, i.e. showing direction of flow of content (i.e. influence, basically) — or much in the way of a time element (i.e. turning static snapshots into dynamic networks). Is that fair, or am I missing something? I imagine it would be fun to see how (e.g.) fake news or political memes propagate through a network?

David: While Twitter provides amazing volumes of data, its programming interface is notorious for the absence of two key sources: the date when follower links are created and the precise path of retweets. The reason for the general picture of snapshots over time is that researchers cannot fully trace back the history of a follower network, they can only monitor it with certain frequency to overcome the fact that links do not have a date attached.

The generally missing picture of flows of information is because when looking up a retweet, we can see the original tweet that is being retweeted, but not if the retweet is of a retweet of a friend. This way, without special access to Twitter data or alternative sources, all information flows look like stars around the original tweet, rather than propagation trees through a social network that allow the precise analysis of fake news or memes.

Ed.: Given all the work on Twitter, how well-placed do you think social scientists would be to advise a political campaign on “how to create an influential network” beyond just the obvious (Tweet well and often, and maybe hire a load of bots). i.e. are there any “general rules” about communication structure that would be practically useful to campaigning organisations?

David: When we talk about influence on Twitter, we usually talk about rather superficial behaviour, such as retweeting content or clicking on a link. This should not be mistaken as a more substantial kind of influence, the kind that makes people change their opinion or go to vote. Evaluating the real impact of Twitter influence is a bottleneck for how much social scientists can advise a political campaign. I would say than rather than providing general rules that can be applied everywhere, social scientists and computer scientists can be much more useful when advising, tracking, and optimizing individual campaigns that take into account the details and idiosyncrasies of the people that might be influenced by the campaign.

Ed.: Random question: but where did “computational social science” emerge from – is it actually quite dependent on Twitter (and Wikipedia?), or are there other commonly-used datasets? And are computational social science, “big data analytics”, and (social) data science basically describing the same thing?

David: Tracing back the meaning and influence of “computational social science” could take a whole book! My impression is that the concept started few decades ago as a spin on “sociophysics”, where the term “computational” was used as in “computational model”, emphasizing a focus on social science away from toy model applications from physics. Then the influential Science article by David Lazer and colleagues in 2009 defined the term as the application of digital trace datasets to test theories from the social sciences, leaving the whole computational modelling outside the frame. In that case, “computational” was used more as it is used in “computational biology”, to refer to social science with increased power and speed thanks to computer-based technologies. Later it seems to have converged back into a combination of both the modelling and the data analysis trends, as in the “Manifesto of computational social science” by Rosaria Conte and colleagues in 2012, inspired by the fact that we need computational modelling techniques from complexity science to understand what we observe in the data.

The Twitter and Wikipedia dependence of the field is just a path dependency due to the ease and open access to those datasets, and a key turning point in the field is to be able to generalize beyond those “model organisms”, as Zeynep Tufekci calls them. One can observe these fads in the latest computer science conferences, with the rising ones being Reddit and Github, or when looking at earlier research that heavily used product reviews and blog datasets. Computational social science seems to be maturing as a field, make sense out of those datasets and not just telling cool data-driven stories about one website or another. Perhaps we are beyond the peak of inflated expectations of the hype curve and the best part is yet to come.

With respect to big data and social data science, it is easy to get lost in the field of buzzwords. Big data analytics only deals with the technologies necessary to process large volumes of data, which could come from any source including social networks but also telescopes, seismographs, and any kind of sensor. These kind of techniques are only sometimes necessary in computational social science, but are far from the core of topics of the field.

Social data science is closer, but puts a stronger emphasis on problem-solving rather than testing theories from the social sciences. When using “data science” we usually try to emphasize a predictive or explorative aspect, rather than the confirmatory or generative approach of computational social science. The emphasis on theory and modelling of computational social science is the key difference here, linking back to my earlier comment about the role of computational modelling and complexity science in the field.

Ed.: Finally, how successful do you think computational social scientists will be in identifying any underlying “social patterns” — i.e. would you agree that the Internet is a “Hadron Collider” for social science? Or is society fundamentally too chaotic and unpredictable?

David: As web scientists like to highlight, the Web (not the Internet, which is the technical infrastructure connecting computers) is the largest socio-technical artifact ever produced by humanity. Rather than as a Hadron Collider, which is a tool to make experiments, I would say that the Web can be the Hubble telescope of social science: it lets us observe human behaviour at an amazing scale and resolution, not only capturing big data but also, fast, long, deep, mixed, and weird data that we never imagined before.

While I doubt that we will be able to predict society in some sort of “psychohistory” manner, I think that the Web can help us to understand much more about ourselves, including our incentives, our feelings, and our health. That can be useful knowledge to make decisions in the future and to build a better world without the need to predict everything.

Read the full article: Garcia, D., Mavrodiev, P., Casati, D., and Schweitzer, F. (2017) Understanding Popularity, Reputation, and Social Influence in the Twitter Society. Policy & Internet 9 (3) doi:10.1002/poi3.151

David Garcia was talking to blog editor David Sutcliffe.

The above map shows the global distribution of the Fentanyl trade on the darknet. The US accounts for almost 40% of global darknet trade, with Canada and Australia at 15% and 12%, respectively. The UK and Germany are the largest sellers in Europe with 9% and 5% of sales. While China is often mentioned as an important source of the drug, it accounts for only 4% of darknet sales. However, this does not necessarily mean that China is not the ultimate site of production. Many of the sellers in places like the US, Canada, and Western Europe are likely intermediaries rather than producers themselves.

In the next few months, we’ll be sharing more visualisations of the economic geographies of products on the darknet. In the meantime you can find out more about our work by Exploring the Darknet in Five Easy Questions.

Follow the project here: https://www.oii.ox.ac.uk/research/projects/economic-geog-darknet/

Twitter: @OiiDarknet

While the online world has turned into an ecosystem of bots (by which we mean computer scripts that automatically handle repetitive and mundane tasks), our knowledge of how these automated agents interact with each other is rather poor. But being automata without capacity for emotions, meaning-making, creativity, or sociality, we might expect bot interactions to be relatively predictable and uneventful.

In their PLOS ONE article “Even good bots fight: The case of Wikipedia“, Milena Tsvetkova, Ruth García-Gavilanes, Luciano Floridi, and Taha Yasseri analyze the interactions between bots that edit articles on Wikipedia. They track the extent to which bots undid each other’s edits over the period 2001–2010, model how pairs of bots interact over time, and identify different types of interaction outcomes. Although Wikipedia bots are intended to support the encyclopaedia — identifying and undoing vandalism, enforcing bans, checking spelling, creating inter-language links, importing content automatically, mining data, identifying copyright violations, greeting newcomers, etc. — the authors find they often undid each other’s edits, with these sterile “fights” sometimes continuing for years.

They suggest that even relatively “dumb” bots may give rise to complex interactions, carrying important implications for Artificial Intelligence research. Understanding these bot-bot interactions will be crucial for managing social media, providing adequate cyber-security, and designing autonomous vehicles (that don’t crash..).

We caught up with Taha Yasseri and Luciano Floridi to discuss the implications of the findings:

Ed.: Is there any particular difference between the way individual bots interact (and maybe get bogged down in conflict), and lines of vast and complex code interacting badly, or having unforeseen results (e.g. flash-crashes in automated trading): i.e. is this just (another) example of us not always being able to anticipate how code interacts in the wild?

Taha: There are similarities and differences. The most notable difference is that here bots are not competing. They all work based on same rules and more importantly to achieve the same goal that is to increase the quality of the encyclopedia. Considering these features, the rather antagonistic interactions between the bots come as a surprise.

Ed.: Wikipedia have said that they know about it, and that it’s a minor problem: but I suppose Wikipedia presents a nice, open, benevolent system to make a start on examining and understanding bot interactions. What other bot-systems are you aware of, or that you could have looked at?

Taha: In terms of content generating bots, Twitter bots have turned out to be very important in terms of online propaganda. The crawlers bots that collect information from social media or the web (such as personal information or email addresses) are also being heavily deployed. In fact we have come up with a first typology of the Internet bots based on their type of action and their intentions (benevolent vs malevolent), that is presented in the article.

Ed.: You’ve also done work on human collaborations (e.g. in the citizen science projects of the Zooniverse) — is there any work comparing human collaborations with bot collaborations — or even examining human-bot collaborations and interactions?

Taha: In the present work we do compare bot-bot interactions with human-human interactions to observe similarities and differences. The most striking difference is in the dynamics of negative interactions. While human conflicts heat up very quickly and then disappear after a while, bots undoing each others’ contribution comes as a steady flow which might persist over years. In the HUMANE project, we discuss the co-existence of humans and machines in the digital world from a theoretical point of view and there we discuss such ecosystems in details.

Ed.: Humans obviously interact badly, fairly often (despite being a social species) .. why should we be particularly worried about how bots interact with each other, given humans seem to expect and cope with social inefficiency, annoyances, conflict and break-down? Isn’t this just more of the same?

Luciano: The fact that bots can be as bad as humans is far from reassuring. The fact that this happens even when they are programmed to collaborate is more disconcerting than what happens among humans when these compete, or fight each other. Here are very elementary mechanisms that through simple interactions generate messy and conflictual outcomes. One may hope this is not evidence of what may happen when more complex systems and interactions are in question. The lesson I learnt from all this is that without rules or some kind of normative framework that promote collaboration, not even good mechanisms ensure a good outcome.

Read the full article: Tsvetkova M, Garcia-Gavilanes R, Floridi, L, Yasseri T (2017) Even good bots fight: The case of Wikipedia. PLoS ONE 12(2): e0171774. doi:10.1371/journal.pone.0171774

Taha Yasseri and Luciano Floridi were talking to blog editor David Sutcliffe.

Easy availability of Twitter data links nicely to a key goal of computational social science. If researchers can find ways to impute user characteristics from social media, then the capabilities of computational social science would be greatly extended. However few papers consider the digital divide among Twitter users. But the question of who uses Twitter has major implications for research attempts to use the content of tweets for inference about population behaviour. Do Twitter users share identical characteristics with the population interest? For what populations are Twitter data actually appropriate?

A new article by Grant Blank published in Social Science Computer Review provides a multivariate empirical analysis of the digital divide among Twitter users, comparing Twitter users and nonusers with respect to their characteristic patterns of Internet activity and to certain key attitudes. It thereby fills a gap in our knowledge about an important social media platform, and it joins a surprisingly small number of studies that describe the population that uses social media.

Comparing British (OxIS survey) and US (Pew) data, Grant finds that generally, British Twitter users are younger, wealthier, and better educated than other Internet users, who in turn are younger, wealthier, and better educated than the offline British population. American Twitter users are also younger and wealthier than the rest of the population, but they are not better educated. Twitter users are disproportionately members of elites in both countries. Twitter users also differ from other groups in their online activities and their attitudes.

Under these circumstances, any collection of tweets will be biased, and inferences based on analysis of such tweets will not match the population characteristics. A biased sample can’t be corrected by collecting more data; and these biases have important implications for research based on Twitter data, suggesting that Twitter data are not suitable for research where representativeness is important, such as forecasting elections or gaining insight into attitudes, sentiments, or activities of large populations.

Read the full article: Blank, G. (2016) The Digital Divide Among Twitter Users and Its Implications for Social Research. Social Science Computer Review. DOI: 10.1177/0894439316671698

We caught up with Grant to explore the implications of the findings:

Ed.: Despite your cautions about lack of representativeness, you mention that the bias in Twitter could actually make it useful to study (for example) elite behaviours: for example in political communication?

Grant: Yes. If you want to study elites and channels of elite influence then Twitter is a good candidate. Twitter data could be used as one channel of elite influence, along with other online channels like social media or blog posts, and offline channels like mass media or lobbying. There is an ecology of media and Twitter is one part.

Ed.: You also mention that Twitter is actually quite successful at forecasting certain offline, commercial behaviours (e.g. box office receipts).

Grant: Right. Some commercial products are disproportionately used by wealthier or younger people. That certainly would include certain forms of mass entertainment like cinema. It also probably includes a number of digital products like smartphones, especially more expensive phones, and wearable devices like a Fitbit. If a product is disproportionately bought by the same population groups that use Twitter then it may be possible to forecast sales using Twitter data. Conversely, products disproportionately used by poorer or older people are unlikely to be predictable using Twitter.

Ed.: Is there a general trend towards abandoning expensive, time-consuming, multi-year surveys and polling? And do you see any long-term danger in that? i.e. governments and media (and academics?) thinking “Oh, we can just get it off social media now”.

Grant: Yes and no. There are certainly people who are thinking about it and trying to make it work. The ease and low cost of social media is very seductive. However, that has to be balanced against major weaknesses. First the population using Twitter (and other social media) is unclear, but it is not a random sample. It is just a population of Twitter users, which is not a population of interest to many.

Second, tweets are even less representative. As I point out in the article, over 40% of people with a Twitter account have never sent a tweet, and the top 15% of users account for 85% of tweets. So tweets are even less representative of any real-world population than Twitter users. What these issues mean is that you can’t calculate measures of error or confidence intervals from Twitter data. This is crippling for many academic and government uses.

Third, Twitter’s limited message length and simple interface tends to give it advantages on devices with restricted input capability, like phones. It is well-suited for short, rapid messages. These characteristics tend to encourage Twitter use for political demonstrations, disasters, sports events, and other live events where reports from an on-the-spot observer are valuable. This suggests that Twitter usage is not like other social media or like email or blogs.

Fourth, researchers attempting to extract the meaning of words have 140 characters to analyze and they are littered with abbreviations, slang, non-standard English, misspellings and links to other documents. The measurement issues are immense. Measurement is hard enough in surveys when researchers have control over question wording and can do cognitive interviews to understand how people interpret words.

With Twitter (and other social media) researchers have no control over the process that generated the data, and no theory of the data generating process. Unlike surveys, social media analysis is not a general-purpose tool for research. Except in limited areas where these issues are less important, social media is not a promising tool.

Ed.: How would you respond to claims that for example Facebook actually had more accurate political polling than anyone else in the recent US Election? (just that no-one had access to its data, and Facebook didn’t say anything)?

Grant: That is an interesting possibility. The problem is matching Facebook data with other data, like voting records. Facebook doesn’t know where people live. Finding their location would not be an easy problem. It is simpler because Facebook would not need an actual address; it would only need to locate the correct voting district or the state (for the Electoral College in US Presidential elections). Still, there would be error of unknown magnitude, probably impossible to calculate. It would be a very interesting research project. Whether it would be more accurate than a poll is hard to say.

Ed.: Do you think social media (or maybe search data) scraping and analysis will ever successfully replace surveys?

Grant: Surveys are such versatile, general purpose tools. They can be used to elicit many kinds information on all kinds of subjects from almost any population. These are not characteristics of social media. There is no real danger that surveys will be replaced in general.

However, I can see certain specific areas where analysis of social media will be useful. Most of these are commercial areas, like consumer sentiments. If you want to know what people are saying about your product, then going to social media is a good, cheap source of information. This is especially true if you sell a mass market product that many people use and talk about; think: films, cars, fast food, breakfast cereal, etc.

These are important topics to some people, but they are a subset of things that surveys are used for. Too many things are not talked about, and some are very important. For example, there is the famous British reluctance to talk about money. Things like income, pensions, and real estate or financial assets are not likely to be common topics. If you are a government department or a researcher interested in poverty, the effect of government assistance, or the distribution of income and wealth, you have to depend on a survey.

There are a lot of other situations where surveys are indispensable. For example, if the OII wanted to know what kind of jobs OII alumni had found, it would probably have to survey them.

Ed.: Finally .. 1400 Twitter articles in .. do we actually know enough now to say anything particularly useful or concrete about it? Are we creeping towards a Twitter revelation or consensus, or is it basically 1400 articles saying “it’s all very complicated”?

Grant: Mostly researchers have accepted Twitter data at face value. Whatever people write in a tweet, it means whatever the researcher thinks it means. This is very easy and it avoids a whole collection of complex issues. All the hard work of understanding how meaning is constructed in Twitter and how it can be measured is yet to be done. We are a long way from understanding Twitter.

Read the full article: Blank, G. (2016) The Digital Divide Among Twitter Users and Its Implications for Social Research. Social Science Computer Review. DOI: 10.1177/0894439316671698

Grant Blank 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).

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.

Negative interactions are also rarely explicitly declared and recorded, making them hard for scientists to study. In their new article on the structural and temporal features of negative interactions in the community, Milena Tsvetkova, Ruth García-Gavilanes and Taha Yasseri use complex network methods to analyze patterns in the timing and configuration of reverts of article edits to Wikipedia. In large online collaboration communities like Wikipedia, users sometimes undo or downrate contributions made by other users; most often to maintain and improve the collaborative project. However, it is also possible that these actions are social in nature, with previous research acknowledging that they could also imply negative social interactions.

The authors find evidence that Wikipedia editors systematically revert the same person, revert back their reverter, and come to defend a reverted editor. However, they don’t find evidence that editors “pay forward” a revert, coordinate with others to revert an editor, or revert different editors serially. These interactions can be related to the status of the editors. Even though the individual reverts might not necessarily be negative social interactions, their analysis points to the existence of certain patterns of negative social dynamics within the editorial community. Some of these patterns have not been previously explored and certainly carry implications for Wikipedia’s own knowledge collection practices — and can also be applied to other large-scale collaboration networks to identify the existence of negative social interactions.

Read the full article: Milena Tsvetkova, Ruth García-Gavilanes and Taha Yasseri (2016) Dynamics of Disagreement: Large-Scale Temporal Network Analysis Reveals Negative Interactions in Online Collaboration. Scientific Reports 6 doi:10.1038/srep36333

We caught up with the authors to explore the implications of the work.

Ed: You find that certain types of negative social interactions and status considerations interfere with knowledge production on Wikipedia. What could or should Wikipedia do about it — or is it not actually a significant problem?

Taha: We believe it is an issue to consider. While the Wikipedia community might not be able to directly cope with it, as negative social interactions are intrinsic to human societies, an important consequence of our report would be to use the information in Wikipedia articles with extra care — and also to bear in mind that Wikipedia content might carry more subjectivity compared to a professionally written encyclopaedia.

Ed: Does reverting behaviour correlate with higher quality articles (i.e. with a lot of editorial attention) or simply with controversial topics — i.e. do you see reverting behaviour as generally a positive or negative thing?

Taha: In a different project we looked at the correlation between controversy and quality. We observed that controversy, up to a certain level, is correlated with higher quality of the article, specifically as far as the completeness of the article is concerned. However, the articles with very high scores of controversy, started to show less quality. In short, a certain amount of controversy helps the articles to become more complete, but too much controversy is a bad sign.

Ed: Do you think your results say more about the structure of Wikipedia, the structure of crowds, or about individuals?

Taha: Our results shed light on some of the most fundamental patterns in human behavior. It is one of the few examples in which a large dataset of negative interactions is analysed and the dynamics of negativity are studied. In this sense, this article is more about human behavior in interaction with other community members in a collaborative environment. However, because our data come from Wikipedia, I believe there are also lessons to be learnt about Wikipedia itself.

Ed: You note that by focusing on the macro-level you miss the nuanced understanding that thick ethnographic descriptions can produce. How common is it for computational social scientists to work with ethnographers? What would you look at if you were to work with ethnographers on this project?

Taha: One of the drawbacks in big data analysis in computational social science is the small depth of the analysis. We are lacking any demographic information about the individuals that we study. We can draw conclusions about the community of Wikipedia editors in a certain language, but that is by no means specific enough. An ethnographic approach, which would benefit our research tremendously, would go deeper in analyzing individuals and studying the features and attributes which lead to certain behavior. For example, we report, at a high level, that “status” determines editors’ actions to a good extend, but of course the mechanisms behind this observation can only be explained based on ethnographic analysis.

Ed: I guess Wikipedia (whether or not unfairly) is commonly associated with edit wars — while obviously also being a gigantic success: how about other successful collaborative platforms — how does Wikipedia differ from Zooniverse, for example?

Taha: There is no doubt that Wikipedia is a huge success and probably the largest collaborative project in the history of mankind. Our research mostly focuses on its dark side, but it does not question its success and value. Compared to other collaborative projects, such as Zooniverse, the main difference is in the management model. Wikipedia is managed and run by the community of editors. Very little top-down management is employed in Wikipedia. Whereas in Zooniverse for instance, the overall structure of the project is designed by a few researchers and the crowd can only act within a pre-determined framework. For more of these sort of comparisons, I suggest to look at our HUMANE project, in which we provide a typology and comparison for a wide range of Human-Machine Networks.

Ed: Finally — do you edit Wikipedia? And have you been on the receiving end of reverts yourself?

Taha: I used to edit Wikipedia much more. And naturally I have had my own share of reverts, at both ends!

Taha Yasseri was talking to blog editor David Sutcliffe.

As Laura Bates, founder of the Everyday Sexism project, has recently highlighted, “it seems to be increasingly difficult to talk about sexism, equality, and women’s rights” (Everyday Sexism Project, 2015). With many theorists suggesting that we have entered a so-called “post-feminist” era in which gender equality has been achieved (cf. McRobbie, 2008; Modleski, 1991), to complain about sexism not only risks being labelled as “uptight”, “prudish”, or a “militant feminist”, but also exposes those who speak out to sustained, and at times vicious, personal attacks (Everyday Sexism Project, 2015). Despite this, thousands of women are speaking out, through Bates’ project, about their experiences of everyday sexism. Our research seeks to draw on the rich history of gender studies in the social sciences, coupling it with emerging computational methods for topic modelling, to better understand the content of reports to the Everyday Sexism Project and the lived experiences of those who post them. Here, we outline the literature which contextualizes our study.

Studies on sexism are far from new. Indeed, particularly amongst feminist theorists and sociologists, the analysis (and deconstruction) of “inequality based on sex or gender categorization” (Harper, 2008) has formed a central tenet of both academic inquiry and a radical politics of female emancipation for several decades (De Beauvoir, 1949; Friedan, 1963; Rubin, 1975; Millett, 1971). Reflecting its feminist origins, historical research on sexism has broadly focused on defining sexist interactions (cf. Glick and Fiske, 1997) and on highlighting the problematic, biologically rooted ‘gender roles’ that form the foundation of inequality between men and women (Millett, 1971; Renzetti and Curran, 1992; Chodorow, 1995).

More recent studies, particularly in the field of psychology, have shifted the focus away from whether and how sexism exists, towards an examination of the psychological, personal, and social implications that sexist incidents have for the women who experience them. As such, theorists such as Matteson and Moradi (2005), Swim et al (2001) and Jost and Kay (2005) have highlighted the damaging intellectual and mental health outcomes for women who are subject to continual experiences of sexism. Atwood, for example, argues in her study of gender bias in families, that sexism combines with other life stressors to create significant psychological distress in women, resulting in them needing to “seek therapy, most commonly for depression and anxiety” (2001, 169).

Given its increasing ubiquity in every day life, it is hardly surprising that the relationship between technology and sexism has also sparked interest from contemporary researchers in the field. Indeed, several studies have explored the intersection between gender and power online, with Susan Herring’s work on gender differences in computer-mediated communication being of particular note (cf. Herring, 2008). Theorists such as Mindi D. Foster have focused on the impact that using digital technology, and particularly Web 2.0 technologies, to talk about sexism can have on women’s well being. Foster’s study found that when women tweeted about sexism, and in particular when they used tweets to a) name the problem, b) criticise it, or c) to suggest change, they viewed their actions as effective and had enhanced life satisfaction, and therefore felt empowered (Foster, 2015: 21).

Despite this diversity of research on sexism, however, there remain some notable gaps in understanding. In particular, as this study hopes to highlight, little previous research on sexism has considered the different ‘types’ of sexism experienced by women (beyond an identification of the workplace and the education system as contexts in which sexism often manifests as per Barnett, 2005; Watkins et al., 2006; Klein, 1992). Furthermore, research focusing on sexism has thus far been largely qualitative in nature. Although a small number of studies have employed quantitative methods (cf. Brandt 2011; Becker and Wright, 2011), none have used computational approaches to analyse the wealth of available online data on sexism.

This project, which will apply a natural language processing approach to analyse data collected from the Everyday Sexism Project website, seeks to fill such a gap. By providing much needed analysis of a large-scale crowd sourced data set on sexism, it is our hope that knowledge gained from this study will advance both the sociological understanding of women’s lived experiences of sexism, and methodological understandings of the suitability of computational topic modelling for conducting this kind of research.

Find out more about the OII’s research on the Everyday Sexism project by visiting the webpage or by looking at the other Policy & Internet blog posts on the project – post 1 and post 2.

Taha Yasseri is a Research Fellow at the OII who has interests in analysis of Big Data to understand human dynamics, government-society interactions, mass collaboration, and opinion dynamics.

Kathryn Eccles is a Research Fellow at the OII who has research interests in the impact of new technologies on scholarly behaviour and research, particularly in the Humanities.

Sophie Melville is a Research Assistant working at the OII. She previously completed the MSc in the Social Science of the Internet at the OII.

References

Atwood, N. C. (2001). Gender bias in families and its clinical implications for women. Social Work, 46 pp. 23–36.

Barnett, R. C. (2005). Ageism and Sexism in the workplace. Generations. 29(3) pp. 25 30.

Bates, Laura. (2015). Everyday Sexism [online] Available at: http://everydaysexism.com [Accessed 1 May 2016].

Becker, Julia C. & Wright, Stephen C. (2011). Yet another dark side of chivalry: Benevolent sexism undermines and hostile sexism motivates collective action for social change. Journal of Personality and Social Psychology, Vol 101(1), Jul 2011, 62-77

Brandt, Mark. (2011). Sexism and Gender Inequality across 57 societies. Psychiological Science. 22(11).

Chodorow, Nancy (1995). “Becoming a feminist foremother”. In Phyllis Chesler, Esther D. Rothblum, Ellen Cole,. Feminist foremothers in women’s studies, psychology, and mental health. New York: Haworth Press. pp. 141–154.

De Beauvoir, Simone. (1949). The second sex, woman as other. London: Vintage.

Foster, M. D. (2015). Tweeting about sexism: The well-being benefits of a social media collective action. British Journal of Social Psychology.

Friedan, Betty. (1963). The Feminine Mystique. W. W. Norton and Co.

Glick, Peter. & Fiske, Susan T. (1997). Hostile and Benevolent Sexism. Psychology of Women Quarterly, 21(1) pp. 119 – 135.

Harper, Amney J. (2008). The relationship between sexism, ambivalent sexism, and relationship quality in heterosexual women. PhD Auburn University.

Herring, Susan C. (2008). Gender and Power in Online Communication. In Janet Holmes and Miriam Meyerhoff (eds) The Handbook of Language and Gender. Oxford: Blackwell.

Jost, J. T., & Kay, A. C. (2005). Exposure to benevolent sexism and complementary gender stereotypes: Consequences for specific and diffuse forms of system justification. Journal of Personality and Social Psychology, 88 pp. 498–509.

Klein, Susan Shurberg. (1992). Sex equity and sexuality in education: breaking the barriers. State University of New York Press.

Matteson, A. V., & Moradi, B. (2005). Examining the structure of the schedule of sexist events: Replication and extension. Psychology of Women Quarterly, 29 pp. 47–57.

McRobbie, Angela (2004). Post-feminism and popular culture. Feminist Media Studies, 4(3) pp. 255 – 264.

Millett, Kate. (1971). Sexual politics. UK: Virago.

Modleski, Tania. (1991). Feminism without women: culture and criticism in a “postfeminist” age. New York: Routledge.

Renzetti, C. and D. Curran, 1992, “Sex-Role Socialization”, in Feminist Philosophies, J. Kourany, J. Sterba, and R. Tong (eds.), New Jersey: Prentice Hall.

Rubin, Gayle. (1975). The traffic in women: notes on the “political economy” of sex. In Rayna R. Reiter (ed.), Toward and anthropology of women. Monthly Review Press.

Swim, J. K., Hyers, L. L., Cohen, L. L., & Ferguson, M. J. (2001). Everyday sexism: Evidence for its incidence, nature, and psychological impact from three daily diary studies. Journal of Social Issues, 57 pp. 31–53.

Watkins et al. (2006). Does it pay to be sexist? The relationship between modern sexism and career outcomes. Journal of Vocational Behaviour. 69(3) pp. 524 – 537.

The Alan Turing Institute will build on the UK’s existing academic strengths in the analysis and application of big data and algorithm research to place the UK at the forefront of world-wide research in data science. The University of Oxford is one of five university partners, and the OII is the only partnering department in the social sciences. The aim of the summit on Data Science for Government and Policy-Making was to understand how government can make better use of big data and the ATI – with the academic partners in listening mode.

We hoped that the participants would bring forward their own stories, hopes and fears regarding data science for the public good. Crucially, we wanted to work out a roadmap for how different stakeholders can work together on the distinct challenges facing government, as opposed to commercial organisations. At the same time, data science research and development has much to gain from the policy-making community. Some of the things that government does – collect tax from the whole population, or give money away at scale, or possess the legitimate use of force – it does by virtue of being government. So the sources of data and some of the data science challenges that public agencies face are unique and tackling them could put government working with researchers at the forefront of data science innovation.

During the Summit a range of stakeholders provided insight from their distinctive perspectives; the Government Chief Scientific Advisor, Sir Mark Walport; Deputy Director of the ATI, Patrick Wolfe; the National Statistician and Director of ONS, John Pullinger; Director of Data at the Government Digital Service, Paul Maltby. Representatives of frontline departments recounted how algorithmic decision-making is already bringing predictive capacity into operational business, improving efficiency and effectiveness.

Discussion revolved around the challenges of how to build core capability in data science across government, rather than outsourcing it (as happened in an earlier era with information technology) or confining it to a data science profession. Some delegates talked of being in the ‘foothills’ of data science. The scale, heterogeneity and complexity of some government departments currently works against data science innovation, particularly when larger departments can operate thousands of databases, creating legacy barriers to interoperability. Out-dated policies can work against data science methodologies. Attendees repeatedly voiced concerns about sharing data across government departments, in some case because of limitations of legal protections; in others because people were unsure what they can and cannot do.

The potential power of data science creates an urgent need for discussion of ethics. Delegates and speakers repeatedly affirmed the importance of an ethical framework and for thought leadership in this area, so that ethics is ‘part of the science’. The clear emergent option was a national Council for Data Ethics (along the lines of the Nuffield Council for Bioethics) convened by the ATI, as recommended in the recent Science and Technology parliamentary committee report The big data dilemma and the government response. Luciano Floridi (OII’s professor of the philosophy and ethics of information) warned that we cannot reduce ethics to mere compliance. Ethical problems do not normally have a single straightforward ‘right’ answer, but require dialogue and thought and extend far beyond individual privacy. There was consensus that the UK has the potential to provide global thought leadership and to set the standard for the rest of Europe. It was announced during the Summit that an ATI Working Group on the Ethics of Data Science has been confirmed, to take these issues forward.

So what happens now?

Throughout the Summit there were calls from policy makers for more data science leadership. We hope that the ATI will be instrumental in providing this, and an interface both between government, business and academia, and between separate Government departments. This Summit showed just how much real demand – and enthusiasm – there is from policy makers to develop data science methods and harness the power of big data. No-one wants to repeat with data science the history of government information technology – where in the 1950s and 60s, government led the way as an innovator, but has struggled to maintain this position ever since. We hope that the ATI can act to prevent the same fate for data science and provide both thought leadership and the ‘time and space’ (as one delegate put it) for policy-makers to work with the Institute to develop data science for the public good.

So since the Summit, in response to the clear need that emerged from the discussion and other conversations with stakeholders, the ATI has been designing a Policy Innovation Unit, with the aim of working with government departments on ‘data science for public good’ issues. Activities could include:

• Secondments at the ATI for data scientists from government
• Short term projects in government departments for ATI doctoral students and postdoctoral researchers
• Developing ATI as an accredited data facility for public data, as suggested in the current Cabinet Office consultation on better use of data in government
• ATI pilot policy projects, using government data
• Policy symposia focused on specific issues and challenges
• ATI representation in regular meetings at the senior level (for example, between Chief Scientific Advisors, the Cabinet Office, the Office for National Statistics, GO-Science).
• ATI acting as an interface between public and private sectors, for example through knowledge exchange and the exploitation of non-government sources as well as government data
• ATI offering a trusted space, time and a forum for formulating questions and developing solutions that tackle public policy problems and push forward the frontiers of data science
• ATI as a source of cross-fertilization of expertise between departments
• Reviewing the data science landscape in a department or agency, identifying feedback loops – or lack thereof – between policy-makers, analysts, front-line staff and identifying possibilities for an ‘intelligent centre’ model through strategic development of expertise.

The Summit, and a series of Whitehall Roundtables convened by GO-Science which led up to it, have initiated a nascent network of stakeholders across government, which we aim to build on and develop over the coming months. If you are interested in being part of this, please do be in touch with us

Helen Margetts, Oxford Internet Institute, University of Oxford (director@oii.ox.ac.uk)

Tom Melham, Department of Computer Science, University of Oxford

In an unprecedented move, the American Statistical Association recently released a statement (March 7 2016) warning against how p-values are currently used. This reflects a growing concern in academic circles that whilst a lot of attention is paid to the huge impact of big data and algorithmic decision-making, there is considerably less focus on the crucial role played by statistics in enabling effective analysis of big data sets, and making sense of the complex relationships contained within them. Because much as datafication has created huge social opportunities, it has also brought to the fore many problems and limitations with current statistical practices. In particular, the deluge of data has made it crucial that we can work out whether studies are ‘significant’. In our paper, published three days before the ASA’s statement, we argued that the most commonly used tool in the social sciences for calculating significance – the p-value – is misused, misunderstood and, most importantly, doesn’t tell us what we want to know.

The basic problem of ‘significance’ is simple: it is simply unpractical to repeat an experiment an infinite number of times to make sure that what we observe is “universal”. The same applies to our sample size: we are often unable to analyse a “whole population” sample and so have to generalize from our observations on a limited size sample to the whole population. The obvious problem here is that what we observe is based on a limited number of experiments (sometimes only one experiment) and from a limited size sample, and as such could have been generated by chance rather than by an underlying universal mechanism! We might find it impossible to make the same observation if we were to replicate the same experiment multiple times or analyse a larger sample. If this is the case then we will mischaracterise what is happening – which is a really big problem given the growing importance of ‘evidence-based’ public policy. If our evidence is faulty or unreliable then we will create policies, or intervene in social settings, in an equally faulty way.

The way that social scientists have got round this problem (that samples might not be representative of the population) is through the ‘p-value’. The p-value tells you the probability of making a similar observation in a sample with the same size and in the same number of experiments, by pure chance In other words,  it is actually telling you is how likely it is that you would see the same relationship between X and Y even if no relationship exists between them. On the face of it this is pretty useful, and in the social sciences we normally say that a p-value of 1 in 20 means the results are significant. Yet as the American Statistical Association has just noted, even though they are incredibly widespread many researchers mis-interpret what p-values really mean.

In our paper we argued that p-values are misunderstood and misused because people think the p-value tells you much more than it really does. In particular, people think the p-value tells you (i) how likely it is that a relationship between X and Y really exists and (ii) the percentage of all findings that are false (which is actually something different called the False Discovery Rate). As a result, we are far too confident that academic studies are correct. Some commentators have argued that at least 30% of studies are wrong because of problems related to p-values: a huge figure. One of the main problems is that p-values can be ‘hacked’ and as such easily manipulated to show significance when none exists.

If we are going to base public policy (and as such public funding) on ‘evidence’ then we need to make sure that the evidence used is reliable. P-values need to be used far more rigorously, with significance levels of 0.01 or 0.001 seen as standard. We also need to start being more open and transparent about how results are recorded. It is a fine line between data exploration (a legitimate academic exercise) and ‘data dredging’ (where results are manipulated in order to find something noteworthy). Only if researchers are honest about what they are doing will we be able to maximise the potential benefits offered by Big Data. Luckily there are some great initiatives – like the Open Science Framework – which improve transparency around the research process, and we fully endorse researchers making use of these platforms.

Scientific knowledge advances through corroboration and incremental progress, and it is crucial that we use and interpret statistics appropriately to ensure this progress continues. As our knowledge and use of big data methods increase, we need to ensure that our statistical tools keep pace.

Read the full paper: Vidgen, B. and Yasseri, T., (2016) P-values: Misunderstood and Misused, Frontiers in Physics, 4:6. http://dx.doi.org/10.3389/fphy.2016.00006

Bertie Vidgen is a doctoral student at the Oxford Internet Institute researching far-right extremism in online contexts. He is supervised by Dr Taha Yasseri, a research fellow at the Oxford Internet Institute interested in how Big Data can be used to understand human dynamics, government-society interactions, mass collaboration, and opinion dynamics.

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.

At its very core, topic modelling is a technique that seeks to automatically discover the topics contained within a group of documents. ‘Documents’ in this context could refer to text items as lengthy as individual books, or as short as sentences within a paragraph. Let’s take the idea of sentences-as-documents as an example:

• Document 1: I like to eat kippers for breakfast.
• Document 2: I love all animals, but kittens are the cutest.
• Document 3: My kitten eats kippers too.

Assuming that each sentence contains a mixture of different topics (and that a ‘topic’ can be understood as a collection of words (of any part of speech) that have different probabilities of appearance in passages discussing the topic), how does the topic modelling algorithm ‘discover’ the topics within these sentences?

The algorithm is initiated by setting the number of topics that it needs to extract. Of course, it is hard to guess this number without having an insight on the topics, but one can think of this as a resolution tuning parameter. The smaller the number of topics is set, the more general the bag of words in each topic would be, and the looser the connections between them.

The algorithm loops through all of the words in each document, assigning every word to one of our topics in a temporary and semi-random manner. This initial assignment is arbitrary and it is easy to show that different initializations lead to the same results in long run. Once each word has been assigned a temporary topic, the algorithm then re-iterates through each word in each document to update the topic assignment using two criteria: 1) How prevalent is the word in question across topics? And 2) How prevalent are the topics in the document?

To quantify these two, the algorithm calculates the likelihood of the words appearing in each document assuming the assignment of words to topics and topics to documents. 

Of course words can appear in different topics and more than one topic can appear in a document. But the iterative algorithm seeks to maximize the self-consistency of the assignment by maximizing the likelihood of the observed word-document statistics. 

We can illustrate this process and its outcome by going back to our example. A topic modelling approach might use the process above to discover the following topics across our documents:

• Document 1: I like to eat kippers for breakfast. [100% Topic A]
• Document 2: I love all animals, but kittens are the cutest. [100% Topic B]
• Document 3: My kitten eats kippers too. [67% Topic A, 33% Topic B]

Topic modelling defines each topic as a so-called ‘bag of words’, but it is the researcher’s responsibility to decide upon an appropriate label for each topic based on their understanding of language and context. Going back to our example, the algorithm might classify the underlined words under Topic A, which we could then label as ‘food’ based on our understanding of what the words mean. Similarly the italicised words might be classified under a separate topic, Topic B, which we could label ‘animals’. In this simple example the word “eat” has appeared in a sentence dominated by Topic A, but also in a sentence with some association to Topic B. Therefore it can also be seen as a connector of the two topics. Of course animals eat too and they like food!

We are going to use a similar approach to first extract the main topics reflected on the reports to the Everyday Sexism Project website and extract the relation between the sexism-related topics and concepts based on the overlap between the bags of words of each topic. Finally we can also look into the co-appearance of topics in the same document.  This way we try to draw a linguistic picture of the more than 100,000 submitted reports.

As ever, be sure to check back for further updates on our progress!

Homejoy was slated to become the Uber of domestic cleaning services. It was a platform that allowed customers to summon a cleaner as easily as they could hail a ride. Regular cleanups were just as easy to schedule. Ratings from previous clients attested to the skill and trustworthiness of each cleaner. There was no need to go through a cleaning services agency, or scour local classifieds to find a cleaner directly: the platform made it easy for both customers and people working as cleaners to find each other. Homejoy made its money by taking a cut out of each transaction. Given how incredibly successful Uber and Airbnb had been in applying the same model to their industries, Homejoy was widely expected to become the next big success story. It was to be the next step in the inexorable uberization of every industry in the economy.

On 17 July 2015, Homejoy announced that it was shutting down. Usage had grown slower than expected, revenues remained poor, technical glitches hurt operations, and the company was being hit with lawsuits on contractor misclassification. Investors’ money and patience had finally ran out. Journalists wrote interesting analyses of Homejoy’s demise (Forbes, TechCrunch, Backchannel). The root causes of any major business failure (or indeed success) are complex and hard to pinpoint. However, one of the possible explanations identified in these stories stands out, because it corresponds strongly with what theory on platforms and markets could have predicted. Homejoy wasn’t growing and making money because clients and cleaners were taking their relationships off-platform: after making the initial contact through Homejoy, they would simply exchange contact details and arrange further cleanups directly, taking the platform and its revenue share out of the loop. According to Forbes, only 15-20 percent of customers came back to Homejoy within a month to arrange another cleanup.

According to the theory of platforms in economics and management studies literature, platforms solve coordination problems. Digital service platforms like Uber and Airbnb solve, in particular, the problem of finding another party to transact with. Through marketing and bootstrapping efforts they ensure that both buyers and sellers sign up to the platform, and then provide match-making mechanisms to bring them together. They also provide solutions towards the problem of opportunism, that is, how to avoid being cheated by the other party. Rating systems are their main tool in this.

Platforms must compete against the existing institutional arrangements in their chosen industry. Uber has been very successful in taking away business from government-licensed taxicabs. Airbnb has captured market share from hotels and hotel booking sites. Both have also generated lots of new business: transactions that previously didn’t happen at all. It’s not that people didn’t already occassionally pay a highschool friend to give them a ride home from a party, or rent a room for the weekend from a friend of a friend who lives in New York. It’s that platforms make similar things possible even when the highschool friend is not available, or if you simply don’t know anyone with a flat in New York. Platforms coordinate people to turn what is otherwise a thin market into a thick one. Not only do platforms help you to find a stranger to transact with, but they also help you to trust that stranger.

Now consider the market for home cleaning services. Home cleaning differs from on-demand transport and short-term accommodation in one crucial way: the service is typically repeated. Through repeated interactions, the buyer and the seller develop trust in each other. They also develop knowledge capital specific to that particular relationship. The buyer might invest time into communicating their preferences and little details about their home to the seller, while the seller will gradually become more efficient at cleaning that particular home. They have little need for the platform to discipline each individual cleanup; relationships are thus soon taken off-platform. Instead of an all-encompassing Uber-style platform, all that may be needed is a classifieds site or a conventional agency that provides the initial introduction and references. Contrast this with on-demand transport and short-term accommodation, where each transaction is unique and thus each time the counterparty is a stranger — and as such a potential cheat or deadbeat. Here the platform continues to provide security after the parties have been introduced.

The case of Homejoy and the economic theory on platforms thus suggest that there are fundamental limits to the uberization of the economy. Digital service platforms can be very successful at mediating one-off transactions, but they are much less useful in industries where the exact same service is repeated many times, and where buyers and sellers develop assets specific to the relationship. Such industries are more likely to continue to be shaped by hierarchies and networks of personal relationships.

There are probably other dimensions that are also pivotal in predicting whether an industry is susceptible to uberization. Geographical span is one: there are efficiencies to be had from particular cleaners specializing in particular neighbourhoods. Yet, at the same time, online labour platforms like Upwork cater to buyers and sellers of software development (and other digitally mediated contract work) across national boundaries. I will discuss this dimension in detail in a future blog post.

Vili Lehdonvirta is a Research Fellow at the OII. He is an economic sociologist who studies the design and socioeconomic implications of digital marketplaces and platforms, using conventional social research methods as well as novel data science approaches. Read Vili’s other Policy & Internet Blog posts on Uber and Airbnb:

Uber and Airbnb make the rules now – but to whose benefit?

Why are citizens migrating to Uber and Airbnb, and what should governments do about it?

Should we love Uber and Airbnb or protest against them?

When barrister Charlotte Proudman recently spoke out regarding a sexist comment that she had received on the professional networking website LinkedIn, hundreds of women praised her actions in highlighting the issue of workplace sexism – and many of them began to tell similar stories of their own. It soon became apparent that Proudman was not alone in experiencing this kind of sexism, a fact further corroborated by Laura Bates of the Everyday Sexism Project, who asserted that workplace harassment is “the most reported kind of incident” on the project’s UK website.

Proudman’s experience and Bates’ comments on the number of submissions to her site concerning harassment at work provokes a conversation about the nature of sexism, not only in the UK but also at a global level. We know that since its launch in 2012, the Everyday Sexism Project has received over 100,000 submissions in more than 13 different languages, concerning a variety of topics. But what are these topics? As Bates has stated, in the UK, workplace sexism is the most commonly discussed subject on the website – but is this also the case for the Everyday Sexism sites in France, Japan, or Brazil? What are the most common types of sexism globally, and (how) do they relate to each other? Do experiences of sexism change from one country to another?

The multi-lingual reports submitted to the Everyday Sexism project are undoubtedly a gold mine of crowdsourced information with great potential for answering important questions about instances of sexism worldwide, as well as drawing an overall picture of how sexism is experienced in different societies. So far much of the research relating to the Everyday Sexism project has focused on qualitative content analysis, and has been limited to the submissions written in English. Along with Principal Investigators Taha Yasseri and Kathryn Eccles, I will be acting as Research Assistant on a new project funded by the John Fell Oxford University Press Research Fund, that hopes to expand the methods used to investigate Everyday Sexism submission data, by undertaking a large-scale computational study that will enrich existing qualitative work in this area.

Entitled “Semantic Mapping of Sexism: Topic Modelling of Everyday Sexism Content”, our project will take a Natural Language Processing approach, analysing the content of Everyday Sexism reports in different languages, and using topic-modelling techniques to extract the most commonly occurring sexism-related topics and concepts from the submissions. We will map the semantic relations between those topics within and across different languages, comparing and contrasting the ways in which sexism is experienced in everyday life in different cultures and geographies. Ultimately, we hope to create the first data-driven map of sexism on a global scale, forming a solid framework for future studies in growing fields such as online inequality, cyber bullying, and social well being.

We’re very excited about the project and will be charting our progress via the Policy and Internet Blog, so make sure to check back for further updates!

Work on big data for cities is increasingly incorporated under the rubric of “smart cities”. The smart city is an old(ish) idea: give urban policymakers real time information on a whole variety of indicators about their city (from traffic and pollution to park usage and waste bin collection) and they will be able to improve decision making and optimise service delivery. But the initial vision, which mostly centred around adding sensors and RFID tags to objects around the city so that they would be able to communicate, has thus far remained unrealised (big up front investment needs and the requirements of IPv6 are perhaps the most obvious reasons for this).

The rise of big data – large, heterogeneous datasets generated by the increasing digitisation of social life – has however breathed new life into the smart cities concept. If all the cars have GPS devices, all the people have mobile phones, and all opinions are expressed on social media, then do we really need the city to be smart at all? Instead, policymakers can simply extract what they need from a sea of data which is already around them. And indeed, data from mobile phone operators has already been used for traffic optimisation, Oyster card data has been used to plan London Underground service interruptions, sewage data has been used to estimate population levels … the examples go on.

However, at the moment these examples remain largely anecdotal, driven forward by a few cities rather than adopted worldwide. The big data driven smart city faces considerable challenges if it is to become a default means of policymaking rather than a conversation piece. Getting access to the right data; correcting for biases and inaccuracies (not everyone has a GPS, phone, or expresses themselves on social media); and communicating it all to executives remain key concerns. Furthermore, especially in a context of tight budgets, most local governments cannot afford to experiment with new techniques which may not pay off instantly.

This is the context of two current OII projects in the smart cities field: UrbanData2Decide (2014-2016) and NEXUS (2015-2017). UrbanData2Decide joins together a consortium of European universities, each working with a local city partner, to explore how local government problems can be resolved with urban generated data. In Oxford, we are looking at how open mapping data can be used to estimate alcohol availability; how website analytics can be used to estimate service disruption; and how internal administrative data and social media data can be used to estimate population levels. The best concepts will be built into an application which allows decision makers to access these concepts real time.

NEXUS builds on this work. A collaborative partnership with BT, it will look at how social media data and some internal BT data can be used to estimate people movement and traffic patterns around the city, joining these data into network visualisations which are then displayed to policymakers in a data visualisation application. Both projects fill an important gap by allowing city officials to experiment with data driven solutions, providing proof of concepts and showing what works and what doesn’t. Increasing academic-government partnerships in this way has real potential to drive forward the field and turn the smart city vision into a reality.

OII Resarch Fellow Jonathan Bright is a political scientist specialising in computational and ‘big data’ approaches to the social sciences. His major interest concerns studying how people get information about the political process, and how this is changing in the internet era.

Counts of public Facebook posts mentioning any of the party leaders’ surnames. Data generated by social media can be used to understand political behaviour and institutions on an ongoing basis.[/caption]‘Congratulations to my friend @Messina2012 on his role in the resounding Conservative victory in Britain’ tweeted David Axelrod, campaign advisor to Miliband, to his former colleague Jim Messina, Cameron’s strategy adviser, on May 8th. The former was Obama’s communications director and the latter campaign manager of Obama’s 2012 campaign. Along with other consultants and advisors and large-scale data management platforms from Obama’s hugely successful digital campaigns, Conservative and Labour used an arsenal of social media and digital tools to interact with voters throughout, as did all the parties competing for seats in the 2015 election.

The parties ran very different kinds of digital campaigns. The Conservatives used advanced data science techniques borrowed from the US campaigns to understand how their policy announcements were being received and to target groups of individuals. They spent ten times as much as Labour on Facebook, using ads targeted at Facebook users according to their activities on the platform, geo-location and demographics. This was a top down strategy that involved working out was happening on social media and responding with targeted advertising, particularly for marginal seats. It was supplemented by the mainstream media, such as the Telegraph for example, which contacted its database of readers and subscribers to services such as Telegraph Money, urging them to vote Conservative. As Andrew Cooper tweeted after the election, ‘Big data, micro-targeting and social media campaigns just thrashed “5 million conversations” and “community organizing”’.

He has a point. Labour took a different approach to social media. Widely acknowledged to have the most boots on the real ground, knocking on doors, they took a similar ‘ground war’ approach to social media in local campaigns. Our own analysis at the Oxford Internet Institute shows that of the 450K tweets sent by candidates of the six largest parties in the month leading up to the general election, Labour party candidates sent over 120,000 while the Conservatives sent only 80,000, no more than the Greens and not much more than UKIP. But the greater number of Labour tweets were no more productive in terms of impact (measured in terms of mentions generated: and indeed the final result).

Both parties’ campaigns were tightly controlled. Ostensibly, Labour generated far more bottom-up activity from supporters using social media, through memes like #votecameron out, #milibrand (responding to Miliband’s interview with Russell Brand), and what Miliband himself termed the most unlikely cult of the 21st century in his resignation speech, #milifandom, none of which came directly from Central Office. These produced peaks of activity on Twitter that at some points exceeded even discussion of the election itself on the semi-official #GE2015 used by the parties, as the figure below shows. But the party remained aloof from these conversations, fearful of mainstream media mockery.

The Brand interview was agreed to out of desperation and can have made little difference to the vote (partly because Brand endorsed Miliband only after the deadline for voter registration: young voters suddenly overcome by an enthusiasm for participatory democracy after Brand’s public volte face on the utility of voting will have remained disenfranchised). But engaging with the swathes of young people who spend increasing amounts of their time on social media is a strategy for engagement that all parties ought to consider. YouTubers like PewDiePie have tens of millions of subscribers and billions of video views – their videos may seem unbelievably silly to many, but it is here that a good chunk the next generation of voters are to be found.

Only one of the leaders had a presence on social media that managed anything like the personal touch and universal reach that Obama achieved in 2008 and 2012 based on sustained engagement with social media – Nicola Sturgeon. The SNP’s use of social media, developed in last September’s referendum on Scottish independence had spawned a whole army of digital activists. All SNP candidates started the campaign with a Twitter account. When we look at the 650 local campaigns waged across the country, by far the most productive in the sense of generating mentions was the SNP; 100 tweets from SNP local candidates generating 10 times more mentions (1,000) than 100 tweets from (for example) the Liberal Democrats.

Scottish Labour’s failure to engage with Scottish peoples in this kind of way illustrates how difficult it is to suddenly develop relationships on social media – followers on all platforms are built up over years, not in the short space of a campaign. In strong contrast, advertising on these platforms as the Conservatives did is instantaneous, and based on the data science understanding (through advertising algorithms) of the platform itself. It doesn’t require huge databases of supporters – it doesn’t build up relationships between the party and supporters – indeed, they may remain anonymous to the party. It’s quick, dirty and effective.

The pollsters’ terrible night

So neither of the two largest parties really did anything with social media, or the huge databases of interactions that their platforms will have generated, to generate long-running engagement with the electorate. The campaigns were disconnected from their supporters, from their grass roots.

But the differing use of social media by the parties could lend a clue to why the opinion polls throughout the campaign got it so wrong, underestimating the Conservative lead by an average of five per cent. The social media data that may be gathered from this or any campaign is a valuable source of information about what the parties are doing, how they are being received, and what people are thinking or talking about in this important space – where so many people spend so much of their time. Of course, it is difficult to read from the outside; Andrew Cooper labeled the Conservatives’ campaign of big data to identify undecided voters, and micro-targeting on social media, as ‘silent and invisible’ and it seems to have been so to the polls.

Many voters were undecided until the last minute, or decided not to vote, which is impossible to predict with polls (bar the exit poll) – but possibly observable on social media, such as the spikes in attention to UKIP on Wikipedia towards the end of the campaign, which may have signaled their impressive share of the vote. As Jim Messina put it to msnbc news following up on his May 8^th tweet that UK (and US) polling was ‘completely broken’ – ‘people communicate in different ways now’, arguing that the Miliband campaign had tried to go back to the 1970s.

Surveys – such as polls — give a (hopefully) representative picture of what people think they might do. Social media data provide an (unrepresentative) picture of what people really said or did. Long-running opinion surveys (such as the Ipsos MORI Issues Index) can monitor the hopes and fears of the electorate in between elections, but attention tends to focus on the huge barrage of opinion polls at election time – which are geared entirely at predicting the election result, and which do not contribute to more general understanding of voters. In contrast, social media are a good way to track rapid bursts in mobilization or support, which reflect immediately on social media platforms – and could also be developed to illustrate more long running trends, such as unpopular policies or failing services.

As opinion surveys face more and more challenges, there is surely good reason to supplement them with social media data, which reflect what people are really thinking on an ongoing basis – like, a video in rather than the irregular snapshots taken by polls. As a leading pollster João Francisco Meira, director of Vox Populi in Brazil (which is doing innovative work in using social media data to understand public opinion) put it in conversation with one of the authors in April – ‘we have spent so long trying to hear what people are saying – now they are crying out to be heard, every day’. It is a question of pollsters working out how to listen.

Political big data

Analysts of political behaviour – academics as well as pollsters — need to pay attention to this data. At the OII we gathered large quantities of data from Facebook, Twitter, Wikipedia and YouTube in the lead-up to the election campaign, including mentions of all candidates (as did Demos’s Centre for the Analysis of Social Media). Using this data we will be able, for example, to work out the relationship between local social media campaigns and the parties’ share of the vote, as well as modeling the relationship between social media presence and turnout.

We can already see that the story of the local campaigns varied enormously – while at the start of the campaign some candidates were probably requesting new passwords for their rusty Twitter accounts, some already had an ongoing relationship with their constituents (or potential constituents), which they could build on during the campaign. One of the candidates to take over the Labour party leadership, Chuka Umunna, joined Twitter in April 2009 and now has 100K followers, which will be useful in the forthcoming leadership contest.

Election results inject data into a research field that lacks ‘big data’. Data hungry political scientists will analyse these data in every way imaginable for the next five years. But data in between elections, for example relating to democratic or civic engagement or political mobilization, has traditionally been woefully short in our discipline. Analysis of the social media campaigns in #GE2015 will start to provide a foundation to understand patterns and trends in voting behaviour, particularly when linked to other sources of data, such as the actual constituency-level voting results and even discredited polls — which may yet yield insight, even having failed to achieve their predictive aims. As the OII’s Jonathan Bright and Taha Yasseri have argued, we need ‘a theory-informed model to drive social media predictions, that is based on an understanding of how the data is generated and hence enables us to correct for certain biases’

A political data science

Parties, pollsters and political analysts should all be thinking about these digital disconnects in #GE2015, rather than burying them with their hopes for this election. As I argued in a previous post, let’s use data generated by social media to understand political behaviour and institutions on an ongoing basis. Let’s find a way of incorporating social media analysis into polling models, for example by linking survey datasets to big data of this kind. The more such activity moves beyond the election campaign itself, the more useful social media data will be in tracking the underlying trends and patterns in political behavior.

And for the parties, these kind of ways of understanding and interacting with voters needs to be institutionalized in party structures, from top to bottom. On 8th May, the VP of a policy think-tank tweeted to both Axelrod and Messina ‘Gentlemen, welcome back to America. Let’s win the next one on this side of the pond’. The UK parties are on their own now. We must hope they use the time to build an ongoing dialogue with citizens and voters, learning from the success of the new online interest group barons, such as 38 degrees and Avaaz, by treating all internet contacts as ‘members’ and interacting with them on a regular basis. Don’t wait until 2020!

Helen Margetts is the Director of the OII, and Professor of Society and the Internet. She is a political scientist specialising in digital era governance and politics, investigating political behaviour, digital government and government-citizen interactions in the age of the internet, social media and big data. She has published over a hundred books, articles and major research reports in this area, including Political Turbulence: How Social Media Shape Collective Action (with Peter John, Scott Hale and Taha Yasseri, 2015).

Scott A. Hale is a Data Scientist at the OII. He develops and applies techniques from computer science to research questions in the social sciences. He is particularly interested in the area of human-computer interaction and the spread of information between speakers of different languages online and the roles of bilingual Internet users. He is also interested in collective action and politics more generally.

Yet trace data does not fit into the quantitative / qualitative binary. The trace of a tweet includes textual information, often with links or images and metadata about who sent it, when and sometimes where they were. The traces of web browsing are also largely textual with some audio/visual elements. The quantity of these textual traces often necessitates some kind of initial quantitative filtering, but it doesn’t determine the questions or approach.

The challenges are important to understand and address because the promise of new insight into social life is real. Large-scale patterns become possible to detect, for example according to one study of mobile phone location data one’s future location is 93% predictable (Song, Qu, Blum & Barabási, 2010), despite great variation in the individual patterns. This new finding opens up further possibilities for comparison and understanding the context of these patterns. Are locations more or less predictable among people with different socio-economic circumstances? What are the key differences between the most and least predictable?

Computational social science is often associated with large-scale studies of anonymized users such as the phone location study mentioned above, or participation traces of those who contribute to online discussions. Studies that focus on limited information about a large number of people are only one type, which I call horizontal trace data. Other studies that work in collaboration with informed participants can add context and depth by asking for multiple forms of trace data and involving participants in interpreting them — what I call the vertical trace data approach.

In my doctoral dissertation I took the vertical approach to examining political information gathering during an election, gathering participants’ web browsing data with their informed consent and interviewing them in person about the context (Menchen-Trevino 2012). I found that access to websites with political information was associated with self-reported political interest, but access to election-specific pages was not. The most active election-specific browsing came from those who were undecided on election day, while many of those with high political interest had already decided whom to vote for before the election began. This is just one example of how digging futher into such data can reveal that what is true for larger categories (political information in general) may not be true, and in fact can be misleading for smaller domains (election-specific browsing). Vertical trace data collection is difficult, but it should be an important component of the project of computational social science.

Read the full article: Menchen-Trevino, E. (2013) Collecting vertical trace data: Big possibilities and big challenges for multi-method research. Policy and Internet 5 (3) 328-339.

References

Menchen-Trevino, E. (2013) Collecting vertical trace data: Big possibilities and big challenges for multi-method research. Policy and Internet 5 (3) 328-339.

Menchen-Trevino, E. (2012) Partisans and Dropouts?: News Filtering in the Contemporary Media Environment. Northwestern University, Evanston, Illinois.

Song, C., Qu, Z., Blumm, N., & Barabasi, A.-L. (2010) Limits of Predictability in Human Mobility. Science 327 (5968) 1018–1021.

Erica Menchen-Trevino is an Assistant Professor at Erasmus University Rotterdam in the Media & Communication department. She researches and teaches on topics of political communication and new media, as well as research methods (quantitative, qualitative and mixed).

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.

Over the years, this debate has plodded steadily along, with scholars on both ends of the spectrum fighting furiously to establish a working understanding. Then, the Internet came along and drew up new battle lines. The Internet is at its best when it provides a home for the disenfranchised, an environment where like-minded individuals can wipe free the dust of societal disassociation and connect and share content. However, the Internet brought with it a shift in power, particularly in how individuals conceptualised society and their role within it. The Internet, in addition to this role, provided a plethora of new and customisable modes of political participation. From the onset, a lot of these new forms of engagement were extensions of existing forms, broadening the everyday citizen’s participatory repertoire. There was a move from voting to e-voting, petitions to e-petitions, face-to-face communities to online communities; the Internet took what was already there and streamlined it, removing those pesky elements of time, space and identity.

Yet, as the Internet continues to develop, and we move into the ultra-heightened communicative landscape of the social web, new and unique forms of political participation take root, drawing upon those customisable environments and organic cyber migrations. The most prominent of these is clicktivism, sometimes also, unfairly, referred to as slacktivism. Clicktivism takes the fundamental features of browsing culture and turns them into a means of political expression. Quite simply, clicktivism refers to the simplification of online participatory processes: one-click online petitions, content sharing, social buttons (e.g. Facebook’s ‘Like’ button) etc.

For the most part, clicktivism is seen in derogatory terms, with the idea that the streamlining of online processes has created a societal disposition towards feel-good, ‘easy’ activism. From this perspective, clicktivism is a lazy or overly-convenient alternative to the effort and legitimacy of traditional engagement. Here, individuals engaging in clicktivism may derive some sense of moral gratification from their actions, but clicktivism’s capacity to incite genuine political change is severely limited. Some would go so far as to say that clicktivism has a negative impact on democratic systems, as it undermines an individual’s desire and need to participate in traditional forms of engagement; those established modes which mainstream political scholars understand as the backbone of a healthy, functioning democracy.

This idea that clicktivism isn’t ‘legitimate’ activism is fuelled by a general lack of understanding about what clicktivism actually involves. As a recent development in observed political action, clicktivism has received its fair share of attention in the political participation literature. However, for the most part, this literature has done a poor job of actually defining clicktivism. As such, clicktivism is not so much a contested notion, as an ill-defined one. The extant work continues to describe clicktivism in broad terms, failing to effectively establish what it does, and does not, involve. Indeed, as highlighted, the mainstream political participation literature saw clicktivism not as a specific form of online action, but rather as a limited and unimportant mode of online engagement.

However, to disregard emerging forms of engagement such as clicktivism because they are at odds with long-held notions of what constitutes meaningful ‘political’ engagement is a misguided and dangerous road to travel. Here, it is important that we acknowledge that a political act, even if it requires limited effort, has relevance for the individual, and, as such, carries worth. And this is where we see clicktivism challenging these traditional notions of political participation. To date, we have looked at clicktivism through an outdated lens; an approach rooted in traditional notions of democracy. However, the Internet has fundamentally changed how people understand politics, and, consequently, it is forcing us to broaden our understanding of the ‘political’, and of what constitutes political participation.

The Internet, in no small part, has created a more reflexive political citizen, one who has been given the tools to express dissatisfaction throughout all facets of their life, not just those tied to the political arena. Collective action underpinned by a developed ideology has been replaced by project orientated identities and connective action. Here, an individual’s desire to engage does not derive from the collective action frames of political parties, but rather from the individual’s self-evaluation of a project’s worth and their personal action frames.

Simply put, people now pick and choose what projects they participate in and feel little generalized commitment to continued involvement. And it is clicktivism which is leading the vanguard here. Clicktivism, as an impulsive, non-committed online political gesture, which can be easily replicated and that does not require any specialized knowledge, is shaped by, and reinforces, this change. It affords the project-oriented individual an efficient means of political participation, without the hassles involved with traditional engagement.

This is not to say, however, that clicktivism serves the same functions as traditional forms. Indeed, much more work is needed to understand the impact and effect that clicktivist techniques can have on social movements and political issues. However, and this is the most important point, clicktivism is forcing us to reconsider what we define as political participation. It does not overtly engage with the political arena, but provides avenues through which to do so. It does not incite genuine political change, but it makes people feel as if they are contributing. It does not politicize issues, but it fuels discursive practices. It may not function in the same way as traditional forms of engagement, but it represents the political participation aspirations of the modern citizen. Clicktivism has been bridging the dualism between the traditional and contemporary forms of political participation, and in its place establishing a participatory duality.

Clicktivism, and similar contemporary forms of engagement, are challenging how we understand political participation, and to ignore them because of what they don’t embody, rather than what they do, is to move forward with eyes closed.

Read the full article: Halupka, M. (2014) Clicktivism: A Systematic Heuristic. Policy and Internet 6 (2) 115-132.

Max Halupka is a PhD candidate at the ANZOG Institute for Governance, University of Canberra. His research interests include youth political participation, e-activism, online engagement, hacktivism, and fluid participatory structures.

Ed: Firstly, what is a ‘virtual’ economy? And what exactly are people earning or exchanging in these online environments?

Vili: A virtual economy is an economy that revolves around artificially scarce virtual markers, such as Facebook likes or, in this case, virtual items and currencies in an online game. A lot of what we do online today is rewarded with such virtual wealth instead of, say, money.

Ed: In terms of ‘virtual earning power’ what was the relationship between character gender and user gender?

Vili: We know that in national economies, men and women tend to be rewarded differently for the same amount of work; men tend to earn more than women. Since online economies are such a big part of many people’s lives today, we wanted to know if this holds true in those economies as well. Looking at the virtual economies of two massively-multiplayer online games (MMOG), we found that there are indeed some gender differences in how much virtual wealth players accumulate within the same number of hours played. In one game, EVE Online, male players were on average 11 percent wealthier than female players of the same age, character skill level, and time spent playing. We believe that this finding is explained at least in part by the fact that male and female players tend to favour different activities within the game worlds, what we call “virtual pink and blue collar occupations”. In national economies, this is called occupational segregation: jobs perceived as suitable for men are rewarded differently from jobs perceived as suitable for women, resulting in a gender earnings gap.

However, in another game, EverQuest II, we found that male and female players were approximately equally wealthy. This reflects the fact that games differ in what kind of activities they reward. Some provide a better economic return on fighting and exploring, while others make it more profitable to engage in trading and building social networks. In this respect games differ from national economies, which all tend to be biased towards rewarding male-type activities. Going beyond this particular study, fantasy economies could also help illuminate the processes through which particular occupations come to be regarded as suitable for men or for women, because game developers can dream up new occupations with no prior gender expectations attached.

Ed: You also discussed the distinction between user gender and character gender…

Vili: Besides occupational segregation, there are also other mechanisms that could explain economic gender gaps, like differences in performance or outright discrimination in pay negotiations. What’s interesting about game economies is that people can appear in the guise of a gender that differs from their everyday identity: men can play female characters and vice versa. By looking at player gender and character gender separately, we can distinguish between how “being” female and “appearing to be” female are related to economic outcomes.

We found that in EVE Online, using a female character was associated with slightly less virtual wealth, while in EverQuest II, using a female character was associated with being richer on average. Since in our study the players chose the characters themselves instead of being assigned characters at random, we don’t know what the causal relationship between character gender and wealth in these games was, if any. But it’s interesting to note that again the results differed completely between games, suggesting that while gender does matter, its effect has more to do with the mutable “software” of the players and/or the coded environments rather than our immutable “hardware”.

Ed: The dataset you worked with could be considered to be an example of ‘big data’ (ie you had full transactional trace data people interacting in two games) — what can you discover with this sort of data (as opposed to eg user surveys, participant observation, or ethnographies); and how useful or powerful is it?

Vili: Social researchers are used to working with small samples of data, and then looking at measures of statistical significance to assess whether the findings are generalizable to the overall population or whether they’re just a fluke. This focus on statistical significance is sometimes so extreme that people forget to consider the practical significance of the findings: even if the effect is real, is it big enough to make any difference in practice? In contrast, when you are working with big data, almost any relationship is statistically significant, so that becomes irrelevant. As a result, people learn to focus more on practical significance — researchers, peer reviewers, journal editors, funders, as well as the general public. This is a good thing, because it can increase the impact that social research has in society.

In this study, we spent a lot of time thinking about the practical significance of the findings. In any national economy, a 11 percent gap between men and women would be huge. But in virtual economies, overall wealth inequality tends to be orders of magnitude greater than in national economies, so that a 11 percent gap is in fact relatively minuscule. Other factors, like whether one is a casual participant in the economy or a semi-professional, have a much bigger effect, so much so that I’m not sure if participants notice a gender gap themselves. Thus one of the key conclusions of the study was that we also need to look beyond traditional sociodemographic categories like gender to see what new social divisions may be appearing in virtual economies.

Ed: What do you think are the hot topics and future directions in research (and policy) on virtual economies, gaming, microwork, crowd-sourcing etc.?

Vili: Previously, ICT adoption resulted in some people’s jobs being eliminated and others being enhanced. This shift had uneven impacts on men’s and women’s jobs. Today, we are seeing an Internet-fuelled “volunterization” of some types of work — moving the work from paid employees and contractors to crowds and fans compensated with points, likes, and badges rather than money. Social researchers should keep track of how this shift impacts different social categories like men and women: whose work ends up being compensated in play money, and who gets to keep the conventional rewards.

Read the full article: Lehdonvirta, V., Ratan, R. A., Kennedy, T. L., and Williams, D. (2014) Pink and Blue Pixel$: Gender and Economic Disparity in Two Massive Online Games. The Information Society 30 (4) 243-255.

Vili Lehdonvirta is a Research Fellow and DPhil Programme Director at the Oxford Internet Institute, and an editor of the Policy & Internet journal. He is an economic sociologist who studies the social and economic dimensions of new information technologies around the world, with particular expertise in digital markets and crowdsourcing.

Vili Lehdonvirta was talking to blog editor David Sutcliffe.

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).

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).

This way of thinking about the study has a serious defect, because there are three issues raised by this research: The first is the legality of the study, which, as the authors correctly point out, falls within Facebook users’ giving informed consent when they sign up to the service. Laws or regulation may be required here to prevent this kind of manipulation, but may also be difficult, since it will be hard to draw a line between this experiment and other forms of manipulating peoples’ responses to media. However, Facebook may not want to lose users, for whom this way of manipulating them via their service may ‘cause anxiety’ (as the first author of the study, Adam Kramer, acknowledged in a blog post response to the outcry). In short, it may be bad for business, and hence Facebook may abandon this kind of research (but we’ll come back to this later). But this – companies using techniques that users don’t like, so they are forced to change course – is not new.

The second issue is academic research ethics. This study was carried out by two academic researchers (the other two authors of the study). In retrospect, it is hard to see how this study would have received approval from an institutional review board (IRB), the boards at which academic institutions check the ethics of studies. Perhaps stricter guidelines are needed here since a) big data research is becoming much more prominent in the social sciences and is often based on social media like Facebook, Twitter, and mobile phone data, and b) much – though not all (consider Wikipedia) – of this research therefore entails close relations with the social media companies who provide access to these data, and to being able to experiment with the platforms, as in this case. Here, again, the ethics of academic research may need to be tightened to provide new guidelines for academic collaboration with commercial platforms. But this is not new either.

The third issue, which is the new and important one, is the increasing power that social research using big data has over our lives. This is of course even more difficult to pin down than the first two points. Where does this power come from? It comes from having access to data of a scale and scope that is a leap or step change from what was available before, and being able to perform computational analysis on these data. This is my definition of ‘big data’ (see note 1), and clearly applies in this case, as in other cases we have documented: almost 700000 users’ Facebook newsfeeds were changed in order to perform this experiment, and more than 3 million posts containing more than 122 million words were analysed. The result: it was found that more positive words in Facebook Newsfeeds led to more positive posts by users, and the reverse for negative words.

What is important here are the implications of this powerful new knowledge. To be sure, as the authors point, this was a study that is valuable for social science in showing that emotions may be transmitted online via words, not just in face-to-face situations. But secondly, it also provides Facebook with knowledge that it can use to further manipulate users’ moods; for example, making their moods more positive so that users will come to its – rather than a competitor’s – website. In other words, social science knowledge, produced partly by academic social scientists, enables companies to manipulate peoples’ hearts and minds.

This not the Orwellian world of the Snowden revelations about phone tapping that have been in the news recently. It’s the Huxleyan Brave New World where companies and governments are able to play with peoples’ minds, and do so in a way whereby users may buy into it: after all, who wouldn’t like to have their experience on Facebook improved in a positive way? And of course that’s Facebook’s reply to criticisms of the study: the motivation of the research is that we’re just trying to improve your experience, as Kramer says in his blogpost response cited above. Similarly, according to The Guardian newspaper, ‘A Facebook spokeswoman said the research…was carried out “to improve our services and to make the content people see on Facebook as relevant and engaging as possible”’. But improving experience and services could also just mean selling more stuff.

This is scary, and academic social scientists should think twice before producing knowledge that supports this kind of impact. But again, we can’t pinpoint this impact without understanding what’s new: big data is a leap in how data can be used to manipulate people in more powerful ways. This point has been lost by those who criticize big data mainly on the grounds of the epistemological conundrums involved (as with boy and Crawford’s widely cited paper, see note 2). No, it’s precisely because knowledge is more scientific that it enables more manipulation. Hence, we need to identify the point or points at which we should put a stop to sliding down a slippery slope of increasing manipulation of our behaviours. Further, we need to specify when access to big data on a new scale enables research that affects many people without their knowledge, and regulate this type of research.

Which brings us back to the first point: true, Facebook may stop this kind of research, but how would we know? And have academics therefore colluded in research that encourages this kind of insidious use of data? We can only hope for a revolt against this kind of Huxleyan conditioning, but as in Brave New World, perhaps the outlook is rather gloomy in this regard: we may come to like more positive reinforcement of our behaviours online…

Notes

1. Schroeder, R. 2014. ‘Big Data: Towards a More Scientific Social Science and Humanities?’, in Graham, M., and Dutton, W. H. (eds.), Society and the Internet. Oxford: Oxford University Press, pp.164-76.

2. boyd, D. and Crawford, K. (2012). ‘Critical Questions for big data: Provocations for a cultural, technological and scholarly phenomenon’, Information, Communication and Society, 15(5), 662-79.

Professor Ralph Schroeder has interests in virtual environments, social aspects of e-Science, sociology of science and technology, and has written extensively about virtual reality technology. He is a researcher on the OII project Accessing and Using Big Data to Advance Social Science Knowledge, which follows ‘big data’ from its public and private origins through open and closed pathways into the social sciences, and documents and shapes the ways they are being accessed and used to create new knowledge about the social world.

In the journal’s inaugural issue, founding Editor-in-Chief Helen Margetts outlined what are essentially two central premises behind Policy & Internet’s launch. The first is that “we cannot understand, analyze or make public policy without understanding the technological, social and economic shifts associated with the Internet” (Margetts 2009, 1). It is simply not possible to consider public policy today without some regard for the intertwining of information technologies with everyday life and society. The second premise is that the rise of the Internet is associated with shifts in how policy itself is made. In particular, she proposed that impacts of Internet adoption would be felt in the tools through which policies are effected, and the values that policy processes embody.

The purpose of the Policy and Internet journal was to take up these two challenges: the public policy implications of Internet-related social change, and Internet-related changes in policy processes themselves. In recognition of the inherently multi-disciplinary nature of policy research, the journal is designed to act as a meeting place for all kinds of disciplinary and methodological approaches. Helen predicted that methodological approaches based on large-scale transactional data, network analysis, and experimentation would turn out to be particularly important for policy and Internet studies. Driving the advancement of these methods was therefore the journal’s third purpose. Today, the journal has reached a significant milestone: over one hundred high-quality peer-reviewed articles published. This seems an opportune moment to take stock of what kind of research we have published in practice, and see how it stacks up against the original vision.

At the most general level, the journal’s articles fall into three broad categories: the Internet and public policy (48 articles), the Internet and policy processes (51 articles), and discussion of novel methodologies (10 articles). The first of these categories, “the Internet and public policy,” can be further broken down into a number of subcategories. One of the most prominent of these streams is fundamental rights in a mediated society (11 articles), which focuses particularly on privacy and freedom of expression. Related streams are children and child protection (six articles), copyright and piracy (five articles), and general e-commerce regulation (six articles), including taxation. A recently emerged stream in the journal is hate speech and cybersecurity (four articles). Of course, an enduring research stream is Internet governance, or the regulation of technical infrastructures and economic institutions that constitute the material basis of the Internet (seven articles). In recent years, the research agenda in this stream has been influenced by national policy debates around broadband market competition and network neutrality (Hahn and Singer 2013). Another enduring stream deals with the Internet and public health (eight articles).

Looking specifically at “the Internet and policy processes” category, the largest stream is e-participation, or the role of the Internet in engaging citizens in national and local government policy processes, through methods such as online deliberation, petition platforms, and voting advice applications (18 articles). Two other streams are e-government, or the use of Internet technologies for government service provision (seven articles), and e-politics, or the use of the Internet in mainstream politics, such as election campaigning and communications of the political elite (nine articles). Another stream that has gained pace during recent years, is online collective action, or the role of the Internet in activism, ‘clicktivism,’ and protest campaigns (16 articles). Last year the journal published a special issue on online collective action (Calderaro and Kavada 2013), and the next forthcoming issue includes an invited article on digital civics by Ethan Zuckerman, director of MIT’s Center for Civic Media, with commentary from prominent scholars of Internet activism. A trajectory discernible in this stream over the years is a movement from discussing mere potentials towards analyzing real impacts—including critical analyses of the sometimes inflated expectations and “democracy bubbles” created by digital media (Shulman 2009; Karpf 2012; Bryer 2012).

The final category, discussion of novel methodologies, consists of articles that develop, analyze, and reflect critically on methodological innovations in policy and Internet studies. Empirical articles published in the journal have made use of a wide range of conventional and novel research methods, from interviews and surveys to automated content analysis and advanced network analysis methods. But of those articles where methodology is the topic rather than merely the tool, the majority deal with so-called “big data,” or the use of large-scale transactional data sources in research, commerce, and evidence-based public policy (nine articles). The journal recently devoted a special issue to the potentials and pitfalls of big data for public policy (Margetts and Sutcliffe 2013), based on selected contributions to the journal’s 2012 big data conference: Big Data, Big Challenges? In general, the notion of data science and public policy is a growing research theme.

This brief analysis suggests that research published in the journal over the last five years has indeed followed the broad contours of the original vision. The two challenges, namely policy implications of Internet-related social change and Internet-related changes in policy processes, have both been addressed. In particular, research has addressed the implications of the Internet’s increasing role in social and political life. The journal has also furthered the development of new methodologies, especially the use of online network analysis techniques and large-scale transactional data sources (aka ‘big data’).

As expected, authors from a wide range of disciplines have contributed their perspectives to the journal, and engaged with other disciplines, while retaining the rigor of their own specialisms. The geographic scope of the contributions has been truly global, with authors and research contexts from six continents. I am also pleased to note that a characteristic common to all the published articles is polish; this is no doubt in part due to the high level of editorial support that the journal is able to afford to authors, including copyediting. The justifications for the journal’s establishment five years ago have clearly been borne out, so that the journal now performs an important function in fostering and bringing together research on the public policy implications of an increasingly Internet-mediated society.

And what of my own research interests as an editor? In the inaugural editorial, Helen Margetts highlighted work, finance, exchange, and economic themes in general as being among the prominent areas of Internet-related social change that are likely to have significant future policy implications. I think for the most part, these implications remain to be addressed, and this is an area that the journal can encourage authors to tackle better. As an editor, I will work to direct attention to this opportunity, and welcome manuscript submissions on all aspects of Internet-enabled economic change and its policy implications. This work will be kickstarted by the journal’s 2014 conference (26-27 September), which this year focuses on crowdsourcing and online labor.

Our published articles will continue to be highlighted here in the journal’s blog. Launched last year, we believe this blog will help to expand the reach and impact of research published in Policy and Internet to the wider academic and practitioner communities, promote discussion, and increase authors’ citations. After all, publication is only the start of an article’s public life: we want people reading, debating, citing, and offering responses to the research that we, and our excellent reviewers, feel is important, and worth publishing.

Read the full editorial:  Lehdonvirta, V. (2014) Past and Emerging Themes in Policy and Internet Studies. Policy & Internet 6(2): 109-114.

References

Bryer, T.A. (2011) Online Public Engagement in the Obama Administration: Building a Democracy Bubble? Policy & Internet 3 (4).

Calderaro, A. and Kavada, A. (2013) Challenges and Opportunities of Online Collective Action for Policy Change. Policy & Internet (5) 1.

Hahn, R. and Singer, H. (2013) Is the U.S. Government’s Internet Policy Broken? Policy & Internet 5 (3) 340-363.

Karpf, D. (2012) Online Political Mobilization from the Advocacy Group’s Perspective: Looking Beyond Clicktivism. Policy & Internet 2 (4) 7-41.

Margetts, H. (2009) The Internet and Public Policy. Policy and Internet 1 (1).

Margetts, H. and Sutcliffe, D. (2013) Addressing the Policy Challenges and Opportunities of ‘Big Data.’ Policy & Internet 5 (2) 139-146.

Shulman, S.W. (2009) The Case Against Mass E-mails: Perverse Incentives and Low Quality Public Participation in U.S. Federal Rulemaking. Policy & Internet 1 (1) 23-53.

Blogs are becoming increasingly important for agenda setting and formation of collective public opinion on a wide range of issues. In countries like Russia where the Internet is not technically filtered, but where the traditional media is tightly controlled by the state, they may be particularly important. The Russian language blogosphere counts about 85 million blogs – an amount far beyond the capacities of any government to control – and the Russian search engine Yandex, with its blog rating service, serves as an important reference point for Russia’s educated public in its search of authoritative and independent sources of information. The blogosphere is thereby able to function as a mass medium of “public opinion” and also to exercise influence.

One topic that was particularly salient over the period we studied concerned the Russian Parliamentary elections of December 2011. Widely reported as fraudulent, they provoked immediate and mass street protest action by tens of thousands of people in Moscow and cities and towns across Russia, as well as corresponding activity in the blogosphere. Protesters made effective use of the Internet to organize a movement that demanded cancellation of the parliamentary election results, and the holding of new and fair elections. These protests continued until the following summer, gaining widespread national and international attention.

Most of the political and social discussion blogged in Russia is hosted on the blog platform LiveJournal. Some of these bloggers can claim a certain amount of influence; the top thirty bloggers have over 20,000 “friends” each, representing a good circulation for the average Russian newspaper. Part of the blogosphere may thereby resemble the traditional media; the deeper into the long tail of average bloggers, however, the more it functions as more as pure public opinion. This “top list” effect may be particularly important in societies (like Russia’s) where popularity lists exert a visible influence on bloggers’ competitive behavior and on public perceptions of their significance. Given the influence of these top bloggers, it may be claimed that, like the traditional media, they act as filters of issues to be thought about, and as definers of their relative importance and salience.

Gauging public opinion is of obvious interest to governments and politicians, and opinion polls are widely used to do this, but they have been consistently criticized for the imposition of agendas on respondents by pollsters, producing artefacts. Indeed, the public opinion literature has tended to regard opinion as something to be “extracted” by pollsters, which inevitably pre-structures the output. This literature doesn’t consider that public opinion might also exist in the form of natural language texts, such as blog posts, that have not been pre-structured by external observers.

There are two basic ways to detect topics in natural language texts: the first is manual coding of texts (ie by traditional content analysis), and the other involves rapidly developing techniques of automatic topic modeling or text clustering. The media studies literature has relied heavily on traditional content analysis; however, these studies are inevitably limited by the volume of data a person can physically process, given there may be hundreds of issues and opinions to track — LiveJournal’s 2.8 million blog accounts, for example, generate 90,000 posts daily.

For large text collections, therefore, only the second approach is feasible. In our article we explored how methods for topic modeling developed in computer science may be applied to social science questions – such as how to efficiently track public opinion on particular (and evolving) issues across entire populations. Specifically, we demonstrate how automated topic modeling can identify public agendas, their composition, structure, the relative salience of different topics, and their evolution over time without prior knowledge of the issues being discussed and written about. This automated “discovery” of issues in texts involves division of texts into topically — or more precisely, lexically — similar groups that can later be interpreted and labeled by researchers. Although this approach has limitations in tackling subtle meanings and links, experiments where automated results have been checked against human coding show over 90 percent accuracy.

The computer science literature is flooded with methodological papers on automatic analysis of big textual data. While these methods can’t entirely replace manual work with texts, they can help reduce it to the most meaningful and representative areas of the textual space they help to map, and are the only means to monitor agendas and attitudes across multiple sources, over long periods and at scale. They can also help solve problems of insufficient and biased sampling, when entire populations become available for analysis. Due to their recentness, as well as their mathematical and computational complexity, these approaches are rarely applied by social scientists, and to our knowledge, topic modeling has not previously been applied for the extraction of agendas from blogs in any social science research.

The natural extension of automated topic or issue extraction involves sentiment mining and analysis; as Gonzalez-Bailon, Kaltenbrunner, and Banches (2012) have pointed out, public opinion doesn’t just involve specific issues, but also encompasses the state of public emotion about these issues, including attitudes and preferences. This involves extracting opinions on the issues/agendas that are thought to be present in the texts, usually by dividing sentences into positive and negative. These techniques are based on human-coded dictionaries of emotive words, on algorithmic construction of sentiment dictionaries, or on machine learning techniques.

Both topic modeling and sentiment analysis techniques are required to effectively monitor self-generated public opinion. When methods for tracking attitudes complement methods to build topic structures, a rich and powerful map of self-generated public opinion can be drawn. Of course this mapping can’t completely replace opinion polls; rather, it’s a new way of learning what people are thinking and talking about; a method that makes the vast amounts of user-generated content about society – such as the 65 million blogs that make up the Russian blogosphere — available for social and policy analysis.

Naturally, this approach to public opinion and attitudes is not free of limitations. First, the dataset is only representative of the self-selected population of those who have authored the texts, not of the whole population. Second, like regular polled public opinion, online public opinion only covers those attitudes that bloggers are willing to share in public. Furthermore, there is still a long way to go before the relevant instruments become mature, and this will demand the efforts of the whole research community: computer scientists and social scientists alike.

Read the full paper: Olessia Koltsova and Sergei Koltcov (2013) Mapping the public agenda with topic modeling: The case of the Russian livejournal. Policy and Internet 5 (2) 207–227.

Also read on this blog: Can text mining help handle the data deluge in public policy analysis? by Aude Bicquelet.

References

González-Bailón, S., A. Kaltenbrunner, and R.E. Banches. 2012. “Emotions, Public Opinion and U.S. Presidential Approval Rates: A 5 Year Analysis of Online Political Discussions,” Human Communication Research 38 (2): 121–43.

Taha: Yes we did … actually, we have shown that controversy measures based on “controversial” flags are not inclusive at all and although they might have high precision, they have very low recall. Instead, we constructed an automated algorithm to locate and quantify the editorial wars taking place on the Wikipedia platform. Our algorithm is based on reversions, i.e. when editors undo each other’s contributions. We focused specifically on mutual reverts between pairs of editors and we assigned a maturity score to each editor, based on the total volume of their previous contributions. While counting the mutual reverts, we used more weight for those ones committed by/on editors with higher maturity scores; as a revert between two experienced editors indicates a more serious problem. We always validated our method and compared it with other methods, using human judgement on a random selection of articles.

Ed: Was there any discrepancy between the content deemed controversial by your own quantitative measure, and what the editors themselves had flagged?

Taha: We were able to capture all the flagged content, but not all the articles found to be controversial by our method are flagged. And when you check the editorial history of those articles, you soon realise that they are indeed controversial but for some reason have not been flagged. It’s worth mentioning that the flagging process is not very well implemented in smaller language editions of Wikipedia. Even if the controversy is detected and flagged in English Wikipedia, it might not be in the smaller language editions. Our model is of course independent of the size and editorial conventions of different language editions.

Ed: Were there any differences in the way conflicts arose / were resolved in the different language versions?

Taha: We found the main differences to be the topics of controversial articles. Although some topics are globally debated, like religion and politics, there are many topics which are controversial only in a single language edition. This reflects the local preferences and importances assigned to topics by different editorial communities. And then the way editorial wars initiate and more importantly fade to consensus is also different in different language editions. In some languages moderators interfere very soon, while in others the war might go on for a long time without any moderation.

Ed: In general, what were the most controversial topics in each language? And overall?

Taha: Generally, religion, politics, and geographical places like countries and cities (sometimes even villages) are the topics of debates. But each language edition has also its own focus, for example football in Spanish and Portuguese, animations and TV series in Chinese and Japanese, sex and gender-related topics in Czech, and Science and Technology related topics in French Wikipedia are very often behind editing wars.

Ed: What other quantitative studies of this sort of conflict -ie over knowledge and points of view- are there?

Taha: My favourite work is one by researchers from Barcelona Media Lab. In their paper Jointly They Edit: Examining the Impact of Community Identification on Political Interaction in Wikipedia they provide quantitative evidence that editors interested in political topics identify themselves more significantly as Wikipedians than as political activists, even though they try hard to reflect their opinions and political orientations in the articles they contribute to. And I think that’s the key issue here. While there are lots of debates and editorial wars between editors, at the end what really counts for most of them is Wikipedia as a whole project, and the concept of shared knowledge. It might explain how Wikipedia really works despite all the diversity among its editors.

Ed: How would you like to extend this work?

Taha: Of course some of the controversial topics change over time. While Jesus might stay a controversial figure for a long time, I’m sure the article on President (W) Bush will soon reach a consensus and most likely disappear from the list of the most controversial articles. In the current study we examined the aggregated data from the inception of each Wikipedia-edition up to March 2010. One possible extension that we are working on now is to study the dynamics of these controversy-lists and the positions of topics in them.

Read the full paper: Yasseri, T., Spoerri, A., Graham, M. and Kertész, J. (2014) The most controversial topics in Wikipedia: A multilingual and geographical analysis. In: P.Fichman and N.Hara (eds) Global Wikipedia: International and cross-cultural issues in online collaboration. Scarecrow Press.

Taha was talking to blog editor David Sutcliffe.

Taha Yasseri is the Big Data Research Officer at the OII. Prior to coming to the OII, he spent two years as a Postdoctoral Researcher at the Budapest University of Technology and Economics, working on the socio-physical aspects of the community of Wikipedia editors, focusing on conflict and editorial wars, along with Big Data analysis to understand human dynamics, language complexity, and popularity spread. He has interests in analysis of Big Data to understand human dynamics, government-society interactions, mass collaboration, and opinion dynamics.

Taha: The socially generated transactional data that we call “big data” have been available only very recently; the amount of data we now produce about human activities in a year is comparable to the amount that used to be produced in decades (or centuries). And this is all due to recent advancements in ICTs. Despite the short period of availability of big data, the use of them in different sectors including academia and business has been significant. However, in many cases, the use of big data is limited to monitoring and post hoc analysis of different patterns. Predictive models have been rarely used in combination with big data. Nevertheless, there are very interesting examples of using big data to make predictions about disease outbreaks, financial moves in the markets, social interactions based on human mobility patterns, election results, etc.

Ed: What were the advantages of using Wikipedia as a data source for your study — as opposed to Twitter, blogs, Facebook or traditional media, etc.?

Taha: Our results have shown that the predictive power of Wikipedia page view and edit data outperforms similar box office-prediction models based on Twitter data. This can partially be explained by considering the different nature of Wikipedia compared to social media sites. Wikipedia is now the number one source of online information, and Wikipedia article page view statistics show how much Internet users have been interested in knowing about a specific movie. And the edit counts — even more importantly — indicate the level of interest of the editors in sharing their knowledge about the movies with others. Both indicators are much stronger than what you could measure on Twitter, which is mainly the reaction of the users after watching or reading about the movie. The cost of participation in Wikipedia’s editorial process makes the activity data more revealing about the potential popularity of the movies.

Another advantage is the sheer availability of Wikipedia data. Twitter streams, by comparison, are limited in both size and time. Gathering Facebook data is also problematic, whereas all the Wikipedia editorial activities and page views are recorded in full detail — and made publicly available.

Ed: Could you briefly describe your method and model?

Taha: We retrieved two sets of data from Wikipedia, the editorial activity and the page views relating to our set of 312 movies. The former indicates the popularity of the movie among the Wikipedia editors and the latter among Wikipedia readers. We then defined different measures based on these two data streams (eg number of edits, number of unique editors, etc.) In the next step we combined these data into a linear model that assumes the more popular the movie is, the larger the size of these parameters. However this model needs both training and calibration. We calibrated the model based on the IMBD data on the financial success of a set of ‘training’ movies. After calibration, we applied the model to a set of “test” movies and (luckily) saw that the model worked very well in predicting the financial success of the test movies.

Ed: What were the most significant variables in terms of predictive power; and did you use any content or sentiment analysis?

Taha: The nice thing about this method is that you don’t need to perform any content or sentiment analysis. We deal only with volumes of activities and their evolution over time. The parameter that correlated best with financial success (and which was therefore the best predictor) was the number of page views. I can easily imagine that these days if someone wants to go to watch a movie, they most likely turn to the Internet and make a quick search. Thanks to Google, Wikipedia is going to be among the top results and it’s very likely that the click will go to the Wikipedia article about the movie. I think that’s why the page views correlate to the box office takings so significantly.

Ed: Presumably people are picking up on signals, ie Wikipedia is acting like an aggregator and normaliser of disparate environmental signals — what do you think these signals might be, in terms of box office success? ie is it ultimately driven by the studio media machine?

Taha: This is a very difficult question to answer. There are numerous factors that make a movie (or a product in general) popular. Studio marketing strategies definitely play an important role, but the quality of the movie, the collective mood of the public, herding effects, and many other hidden variables are involved as well. I hope our research serves as a first step in studying popularity in a quantitative framework, letting us answer such questions. To fully understand a system the first thing you need is a tool to monitor and observe it very well quantitatively. In this research we have shown that (for example) Wikipedia is a nice window and useful tool to observe and measure popularity and its dynamics; hopefully leading to a deep understanding of the underlying mechanisms as well.

Ed: Is there similar work / approaches to what you have done in this study?

Taha: There have been other projects using socially generated data to make predictions on the popularity of movies or movement in financial markets, however to the best of my knowledge, it’s been the first time that Wikipedia data have been used to feed the models. We were positively surprised when we observed that these data have stronger predictive power than previously examined datasets.

Ed: If you have essentially shown that ‘interest on Wikipedia’ tracks ‘real-world interest’ (ie box office receipts), can this be applied to other things? eg attention to legislation, political scandal, environmental issues, humanitarian issues: ie Wikipedia as “public opinion monitor”?

Taha: I think so. Now I’m running two other projects using a similar approach; one to predict election outcomes and the other one to do opinion mining about the new policies implemented by governing bodies. In the case of elections, we have observed very strong correlations between changes in the information seeking rates of the general public and the number of ballots cast. And in the case of new policies, I think Wikipedia could be of great help in understanding the level of public interest in searching for accurate information about the policies, and how this interest is satisfied by the information provided online. And more interestingly, how this changes overtime as the new policy is fully implemented.

Ed: Do you think there are / will be practical applications of using social media platforms for prediction, or is the data too variable?

Taha: Although the availability and popularity of social media are recent phenomena, I’m sure that social media data are already being used by different bodies for predictions in various areas. We have seen very nice examples of using these data to predict disease outbreaks or the arrival of earthquake waves. The future of this field is very promising, considering both the advancements in the methodologies and also the increase in popularity and use of social media worldwide.

Ed: How practical would it be to generate real-time processing of this data — rather than analysing databases post hoc?

Taha: Data collection and analysis could be done instantly. However the challenge would be the calibration. Human societies and social systems — similarly to most complex systems — are non-stationary. That means any statistical property of the system is subject to abrupt and dramatic changes. That makes it a bit challenging to use a stationary model to describe a continuously changing system. However, one could use a class of adaptive models or Bayesian models which could modify themselves as the system evolves and more data are available. All these could be done in real time, and that’s the exciting part of the method.

Ed: As a physicist; what are you learning in a social science department? And what does physicist bring to social science and the study of human systems?

Taha: Looking at complicated phenomena in a simple way is the art of physics. As Einstein said, a physicist always tries to “make things as simple as possible, but not simpler”. And that works very well in describing natural phenomena, ranging from sub-atomic interactions all the way to cosmology. However, studying social systems with the tools of natural sciences can be very challenging, and sometimes too much simplification makes it very difficult to understand the real underlying mechanisms. Working with social scientists, I’m learning a lot about the importance of the individual attributes (and variations between) the elements of the systems under study, outliers, self-awarenesses, ethical issues related to data, agency and self-adaptation, and many other details that are mostly overlooked when a physicist studies a social system.

At the same time, I try to contribute the methodological approaches and quantitative skills that physicists have gained during two centuries of studying complex systems. I think statistical physics is an amazing example where statistical techniques can be used to describe the macro-scale collective behaviour of billions and billions of atoms with a single formula. I should admit here that humans are way more complicated than atoms — but the dialogue between natural scientists and social scientists could eventually lead to multi-scale models which could help us to gain a quantitative understanding of social systems, thereby facilitating accurate predictions of social phenomena.

Ed: What database would you like access to, if you could access anything?

Taha: I have day dreams about the database of search queries from all the Internet users worldwide at the individual level. These data are being collected continuously by search engines and technically could be accessed, but due to privacy policy issues it’s impossible to get a hold on; even if only for research purposes. This is another difference between social systems and natural systems. An atom never gets upset being watched through a microscope all the time, but working on social systems and human-related data requires a lot of care with respect to privacy and ethics.

Read the full paper: Mestyán, M., Yasseri, T., and Kertész, J. (2013) Early Prediction of Movie Box Office Success based on Wikipedia Activity Big Data. PLoS ONE 8 (8) e71226.

Taha Yasseri was talking to blog editor David Sutcliffe.

Taha Yasseri is the Big Data Research Officer at the OII. Prior to coming to the OII, he spent two years as a Postdoctoral Researcher at the Budapest University of Technology and Economics, working on the socio-physical aspects of the community of Wikipedia editors, focusing on conflict and editorial wars, along with Big Data analysis to understand human dynamics, language complexity, and popularity spread. He has interests in analysis of Big Data to understand human dynamics, government-society interactions, mass collaboration, and opinion dynamics.

So how can social scientists help policy-makers in this changed environment, ensuring that social science research remains relevant? Social scientists have a good record on having policy influence, indeed in the UK better than other academic fields, including medicine, as recent research from the LSE Public Policy group has shown. Big data hold major promise for social science, which should enable us to further extend our record in policy research. We have access to a cornucopia of data of a kind which is more like that traditionally associated with so-called ‘hard’ science. Rather than being dependent on surveys, the traditional data staple of empirical social science, social media such as Wikipedia, Twitter, Facebook, and Google Search present us with the opportunity to scrape, generate, analyse and archive comparative data of unprecedented quantity. For example, at the OII over the last four years we have been generating a dataset of all petition signing in the UK and US, which contains the joining rate (updated every hour) for the 30,000 petitions created in the last three years. As a political scientist, I am very excited by this kind of data (up to now, we have had big data like this only for voting, and that only at election time), which will allow us to create a complete ecology of petition signing, one of the more popular acts of political participation in the UK. Likewise, we can look at the entire transaction history of online organizations like Wikipedia, or map the link structure of government’s online presence.

But big data holds threats for social scientists too. The technological challenge is ever present. To generate their own big data, researchers and students must learn to code, and for some that is an alien skill. At the OII we run a course on Digital Social Research that all our postgraduate students can take; but not all social science departments could either provide such a course, or persuade their postgraduate students that they needed it. Ours, who study the social science of the Internet, are obviously predisposed to do so. And big data analysis requires multi-disciplinary expertise. Our research team working on petitions data includes a computer scientist (Scott Hale), a physicist (Taha Yasseri) and a political scientist (myself). I can’t imagine doing this sort of research without such technical expertise, and as a multi-disciplinary department we are (reasonably) free to recruit these type of research faculty. But not all social science departments can promise a research career for computer scientists, or physicists, or any of the other disciplinary specialists that might be needed to tackle big data problems.

Five Recommendations for Social Scientists

So, how can social scientists overcome these challenges, and thereby be in a good position to aid policy-makers tackle their own barriers to making the most of the possibilities afforded by big data? Here are five recommendations:

• Accept that multi-disciplinary research teams are going to become the norm for social science research, extending beyond social science disciplines into the life sciences, mathematics, physics, and engineering. At Policy and Internet’s 2012 Big Data conference, the keynote speaker Duncan Watts (physicist turned sociologist) called for a ‘dating agency’ for engineers and social scientists – with the former providing the technological expertise, and the latter identifying the important research questions. We need to make sure that forums exist where social scientists and technologists meet and discuss big data research at the earliest stages, so that research projects and programmes incorporate the core competencies of both.

• We need to provide the normative and ethical basis for policy decisions in the big data era. That means bringing in normative political theorists and philosophers of information into our research teams. The government has committed £65 million to big data research funding, but it seems likely that any successful research proposals will have a strong ethics component embedded in the research programme, rather than an ethics add on or afterthought.

• Training in data science. Many leading US universities are now admitting undergraduates to data science courses, but lack social science input. Of the 20 US masters courses in big data analytics compiled by Information Week, nearly all came from computer science or informatics departments. Social science research training needs to incorporate coding and analysis skills of the kind these courses provide, but with a social science focus. If we as social scientists leave the training to computer scientists, we will find that the new cadre of data scientists tend to leave out social science concerns or questions.

• Bringing policy makers and academic researchers together to tackle the challenges that big data present. Last month the OII and Policy and Internet convened a workshop in Harvard on Responsible Research Agendas for Public Policy in the Big Data Era, which included various leading academic researchers in the government and big data field, and government officials from the Census Bureau, the Federal Reserve Board, the Bureau of Labor Statistics, and the Office of Management and Budget (OMB). The discussions revealed that there is continual procession of major events on big data in Washington DC (usually with a corporate or scientific research focus) to which US federal officials are invited, but also how few were really dedicated to tackling the distinctive issues that face government agencies such as those represented around the table.

• Taking forward theoretical development in social science, incorporating big data insights. I recently spoke at the Oxford Analytica Global Horizons conference, at a session on Big Data. One of the few policy-makers (in proportion to corporate representatives) in the audience asked the panel “where is the theory”? As social scientists, we need to respond to that question, and fast.

This post is based on discussions at the workshop on Responsible Research Agendas for Public Policy in the era of Big Data workshop and the Political Studies Association Why Universities Matter: How Academic Social Science Contributes to Public Policy Impact, held at the LSE on 26 September 2013.

Helen Margetts is the Director of the OII, and Professor of Society and the Internet. She is a political scientist specialising in e-government and digital era governance and politics, investigating the nature and implications of relationships between governments, citizens and the Internet and related digital technologies in the UK and internationally.

On the promise side, such data offers a chance for policy-making and implementation to be more citizen-focused, taking account of citizens’ needs, preferences and actual experience of public services, as recorded on social media platforms. As citizens express policy opinions on social networking sites such as Twitter and Facebook; rate or rank services or agencies on government applications such as NHS Choices; or enter discussions on the burgeoning range of social enterprise and NGO sites, such as Mumsnet, 38 degrees and patientopinion.org, they generate a whole range of data that government agencies might harvest to good use. Policy-makers also have access to a huge range of data on citizens’ actual behaviour, as recorded digitally whenever citizens interact with government administration or undertake some act of civic engagement, such as signing a petition.

Data mined from social media or administrative operations in this way also provide a range of new data which can enable government agencies to monitor – and improve – their own performance, for example through log usage data of their own electronic presence or transactions recorded on internal information systems, which are increasingly interlinked. And they can use data from social media for self-improvement, by understanding what people are saying about government, and which policies, services or providers are attracting negative opinions and complaints, enabling identification of a failing school, hospital or contractor, for example. They can solicit such data via their own sites, or those of social enterprises. And they can find out what people are concerned about or looking for, from the Google Search API or Google trends, which record the search patterns of a huge proportion of internet users.

As for threats, big data is technologically challenging for government, particularly those governments which have always struggled with large-scale information systems and technology projects. The UK government has long been a world leader in this regard and recent events have only consolidated its reputation. Governments have long suffered from information technology skill shortages and the complex skill sets required for big data analytics pose a particularly acute challenge. Even in the corporate sector, over a third of respondents to a recent survey of business technology professionals cited ‘Big data expertise is scarce and expensive’ as their primary concern about using big data software.

And there are particular cultural barriers to government in using social media, with the informal style and blurring of organizational and public-private boundaries which they engender. And gathering data from social media presents legal challenges, as companies like Facebook place barriers to the crawling and scraping of their sites.

More importantly, big data presents new moral and ethical dilemmas to policy makers. For example, it is possible to carry out probabilistic policy-making, where policy is made on the basis of what a small segment of individuals will probably do, rather than what they have done. Predictive policing has had some success particularly in California, where robberies declined by a quarter after use of the ‘PredPol’ policing software, but can lead to a “feedback loop of injustice” as one privacy advocacy group put it, as policing resources are targeted at increasingly small socio-economic groups. What responsibility does the state have to devote disproportionately more – or less – resources to the education of those school pupils who are, probabilistically, almost certain to drop out of secondary education? Such challenges are greater for governments than corporations. We (reasonably) happily trade privacy to allow Tesco and Facebook to use our data on the basis it will improve their products, but if government tries to use social media to understand citizens and improve its own performance, will it be accused of spying on its citizenry in order to quash potential resistance.

And of course there is an image problem for government in this field – discussion of big data and government puts the word ‘big’ dangerously close to the word ‘government’ and that is an unpopular combination. Policy-makers’ responses to Snowden’s revelations of the US Tempora and UK Prism programmes have done nothing to improve this image, with their focus on the use of big data to track down individuals and groups involved in acts of terrorism and criminality – rather than on anything to make policy-making better, or to use the wealth of information that these programmes collect for the public good.

However, policy-makers have no choice but to tackle some of these challenges. Big data has been the hottest trend in the corporate world for some years now, and commentators from IBM to the New Yorker are starting to talk about the big data ‘backlash’. Government has been far slower to recognize the advantages for policy-making and services. But in some policy sectors, big data poses very fundamental questions which call for an answer; how should governments conduct a census, for or produce labour statistics, for example, in the age of big data? Policy-makers will need to move fast to beat the backlash.

This post is based on discussions at the workshop on Responsible Research Agendas for Public Policy in the era of Big Data workshop.

Helen Margetts is the Director of the OII, and Professor of Society and the Internet. She is a political scientist specialising in digital era governance and politics.

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: In basic terms, what patterns of ‘information geography’ are you seeing in the region?

Mark: The first pattern that we see is that the Middle East and North Africa are relatively under-represented in Wikipedia. Even after accounting for factors like population, Internet access, and literacy, we still see less contact than would be expected. Second, of the content that exists, a lot of it is in European and French rather than in Arabic (or Farsi or Hebrew). In other words, there is even less in local languages.

And finally, if we look at contributions (or edits), not only do we also see a relatively small number of edits originating in the region, but many of those edits are being used to write about other parts of the word rather than their own region. What this broadly seems to suggest is that the participatory potentials of Wikipedia aren’t yet being harnessed in order to even out the differences between the world’s informational cores and peripheries.

Ed: How closely do these online patterns in representation correlate with regional (offline) patterns in income, education, language, access to technology (etc.) Can you map one to the other?

Mark: Population and broadband availability alone explain a lot of the variance that we see. Other factors like income and education also play a role, but it is population and broadband that have the greatest explanatory power here. Interestingly, it is most countries in the MENA region that fail to fit well to those predictors.

Ed: How much do you think these patterns result from the systematic imposition of a particular view point – such as official editorial policies – as opposed to the (emergent) outcome of lots of users and editors acting independently?

Mark: Particular modes of governance in Wikipedia likely do play a factor here. The Arabic Wikipedia, for instance, to combat vandalism has a feature whereby changes to articles need to be reviewed before being made public. This alone seems to put off some potential contributors. Guidelines around sourcing in places where there are few secondary sources also likely play a role.

Ed: How much discussion (in the region) is there around this issue? Is this even acknowledged as a fact or problem?

Mark: I think it certainly is recognised as an issue now. But there are few viable alternatives to Wikipedia. Our goal is hopefully to identify problems that lead to solutions, rather than simply discouraging people from even using the platform.

Ed: This work has been covered by the Guardian, Wired, the Huffington Post (etc.) How much interest has there been from the non-Western press or bloggers in the region?

Mark: There has been a lot of coverage from the non-Western press, particularly in Latin America and Asia. However, I haven’t actually seen that much coverage from the MENA region.

Ed: As an academic, do you feel at all personally invested in this, or do you see your role to be simply about the objective documentation and analysis of these patterns?

Mark: I don’t believe there is any such thing as ‘objective documentation.’ All research has particular effects in and on the world, and I think it is important to be aware of the debates, processes, and practices surrounding any research project. Personally, I think Wikipedia is one of humanity’s greatest achievements. No previous single platform or repository of knowledge has ever even come close to Wikipedia in terms of its scale or reach. However, that is all the more reason to critically investigate what exactly is, and isn’t, contained within this fantastic resource. By revealing some of the biases and imbalances in Wikipedia, I hope that we’re doing our bit to improving it.

Ed: What factors do you think would lead to greater representation in the region? For example: is this a matter of voices being actively (or indirectly) excluded, or are they maybe just not all that bothered?

Mark: This is certainly a complicated question. I think the most important step would be to encourage participation from the region, rather than just representation of the region. Some of this involves increasing some of the enabling factors that are the prerequisites for participation; factors like: increasing broadband access, increasing literacy, encouraging more participation from women and minority groups.

Some of it is then changing perceptions around Wikipedia. For instance, many people that we spoke to in the region framed Wikipedia as an American our outside project rather than something that is locally created. Unfortunately we seem to be currently stuck in a vicious cycle in which few people from the region participate, therefore fulfilling the very reason why some people think that they shouldn’t participate. There is also the issue of sources. Not only does Wikipedia require all assertions to be properly sourced, but secondary sources themselves can be a great source of raw informational material for Wikipedia articles. However, if few sources about a place exist, then it adds an additional burden to creating content about that place. Again, a vicious cycle of geographic representation.

My hope is that by both working on some of the necessary conditions to participation, and engaging in a diverse range of initiatives to encourage content generation, we can start to break out of some of these vicious cycles.

Ed: The final moonshot question: How would you like to extend this work; time and money being no object?

Mark: Ideally, I’d like us to better understand the geographies of representation and participation outside of just the MENA region. This would involve mixed-methods (large scale big data approaches combined with in-depth qualitative studies) work focusing on multiple parts of the world. More broadly, I’m trying to build a research program that maintains a focus on a wide range of Internet and information geographies. The goal here is to understand participation and representation through a diverse range of online and offline platforms and practices and to share that work through a range of publicly accessible media: for instance the ‘Atlas of the Internet’ that we’re putting together.

Mark Graham was talking to blog editor David Sutcliffe.

Mark Graham is a Senior Research Fellow at the OII. His research focuses on Internet and information geographies, and the overlaps between ICTs and economic development.