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.

Ed: Josh, political science has long posed a distinction between public spaces and private ones. Yet it seems like many platforms on the Internet, such as Facebook, cannot really be categorized in such terms. If this correct, what does it mean for how we should police and govern the Internet?

Josh: I think that is right – many online spaces are neither public nor private. This is also an issue for some for privacy legal frameworks (especially in the US).. A lot of the covenants and agreements were written forty or fifty years ago, long before anyone had really thought about the Internet. That has now forced governments, societies and parliaments to adapt these existing rights and protocols for the online sphere. I think that we have some fairly clear laws about the use of human intelligence sources, and police law in the offline sphere. The interesting question is how we can take that online. How can the pre-existing standards, like the requirement that procedures are necessary and proportionate, or the ‘right to appeal’, be incorporated into online spaces? In some cases there are direct analogies. In other cases there needs to be some re-writing of the rule book to try figure out what we mean. And, of course, it is difficult because the internet itself is always changing!

Ed: So do you think that concepts like proportionality and justification need to be updated for online spaces?

Josh: I think that at a very basic level they are still useful. People know what we mean when we talk about something being necessary and proportionate, and about the importance of having oversight. I think we also have a good idea about what it means to be non-discriminatory when applying the law, though this is one of those areas that can quickly get quite tricky. Consider the use of online data sources to identify people. On the one hand, the Internet is ‘blind’ in that it does not automatically codify social demographics. In this sense it is not possible to profile people in the same way that we can offline. On the other hand, it is in some ways the complete opposite. It is very easy to directly, and often invisibly, create really firm systems of discrimination – and, most problematically, to do so opaquely.

This is particularly challenging when we are dealing with extremism because, as we pointed out in the report, extremists are generally pretty unremarkable in terms of demographics. It perhaps used to be true that extremists were more likely to be poor or to have had challenging upbringings, but many of the people going to fight for the Islamic State are middle class. So we have fewer demographic pointers to latch onto when trying to find these people. Of course, insofar as there are identifiers they won’t be released by the government. The real problem for society is that there isn’t very much openness and transparency about these processes.

Ed: Governments are increasingly working with the private sector to gain access to different types of information about the public. For example, in Australia a Telecommunications bill was recently passed which requires all telecommunication companies to keep the metadata – though not the content data – of communications for two years. A lot of people opposed the Bill because metadata is still very informative, and as such there are some clear concerns about privacy. Similar concerns have been expressed in the UK about an Investigatory Powers Bill that would require new Internet Connection Records about customers, online activities.  How much do you think private corporations should protect people’s data? And how much should concepts like proportionality apply to them?

Ian: To me the distinction between metadata and content data is fairly meaningless. For example, often just knowing when and who someone called and for how long can tell you everything you need to know! You don’t have to see the content of the call. There are a lot of examples like this which highlight the slightly ludicrous nature of distinguishing between metadata and content data. It is all data. As has been said by former US CIA and NSA Director Gen. Michael Hayden, “we kill people based on metadata.”

One issue that we identified in the report is the increased onus on companies to monitor online spaces, and all of the legal entanglements that come from this given that companies might not be based in the same country as the users. One of our interviewees called this new international situation a ‘very different ballgame’. Working out how to deal with problematic online content is incredibly difficult, and some huge issues of freedom of speech are bound up in this. On the one hand, there is a government-led approach where we use the law to take down content. On the other hand is a broader approach, whereby social networks voluntarily take down objectionable content even if it is permissible under the law. This causes much more serious problems for human rights and the rule of law.

Read the full report: Brown, I., and Cowls, J., (2015) Check the Web: Assessing the Ethics and Politics of Policing the Internet for Extremist Material. VOX-Pol Publications.

Ian Brown is Professor of Information Security and Privacy at the OII. His research is focused on surveillance, privacy-enhancing technologies, and Internet regulation.

Josh Cowls is a a student and researcher based at MIT, working to understand the impact of technology on politics, communication and the media.

Josh and Ian were talking to Blog Editor Bertie Vidgen.

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?

*please note that the views given here do not necessarily reflect the content of the report, or those of the lead author, Ian Brown.

Ed: Josh, could you let us know the purpose of the report, outline some of the key findings, and tell us how you went about researching the topic?

Josh: Sure. In the report we take a step back from the ground-level question of ‘what are the police doing?’ and instead ask, ‘what are the ethical and political boundaries, rationale and justifications for policing the web for these kinds of activity?’ We used an international human rights framework as an ethical and legal basis to understand what is being done. We also tried to further the debate by clarifying a few things: what has already been done by law enforcement, and, really crucially, what the perspectives are of all those involved, including lawmakers, law enforcers, technology companies, academia and many others.

We derived the insights in the report from a series of workshops, one of which was held as part of the EU-funded VOX-Pol network. The workshops involved participants who were quite high up in law enforcement, the intelligence agencies, the tech industry civil society, and academia. We followed these up with interviews with other individuals in similar positions and conducted background policy research.

Ed: You highlight that many extremist groups (such as Isis) are making really significant use of online platforms to organize, radicalize people, and communicate their messages.

Josh: Absolutely. A large part of our initial interest when writing the report lay in finding out more about the role of the Internet in facilitating the organization, coordination, recruitment and inspiration of violent extremism. The impact of this has been felt very recently in Paris and Beirut, and many other places worldwide. This report pre-dates these most recent developments, but was written in the context of these sorts of events.

Given the Internet is so embedded in our social lives, I think it would have been surprising if political extremist activity hadn’t gone online as well. Of course, the Internet is a very powerful tool and in the wrong hands it can be a very destructive force. But other research, separate from this report, has found that the Internet is not usually people’s first point of contact with extremism: more often than not that actually happens offline through people you know in the wider world. Nonetheless it can definitely serve as an incubator of extremism and can serve to inspire further attacks.

Ed: In the report you identify different groups in society that are affected by, and affecting, issues of extremism, privacy, and governance – including civil society, academics, large corporations and governments

Josh: Yes, in the later stages of the report we do divide society into these groups, and offer some perspectives on what they do, and what they think about counter-extremism. For example, in terms of counter-speech there are different roles for government, civil society, and industry. There is this idea that ISIS are really good at social media, and that that is how they are powering a lot of their support; but one of the people that we spoke to said that it is not the case that ISIS are really good, it is just that governments are really bad!

We shouldn’t ask government to participate in the social network: bureaucracies often struggle to be really flexible and nimble players on social media. In contrast, civil society groups tend to be more engaged with communities and know how to “speak the language” of those who might be vulnerable to radicalization. As such they can enter that dialogue in a much more informed and effective way.

The other tension, or paradigm, that we offer in this report is the distinction between whether people are ‘at risk’ or ‘a risk’. What we try to point to is that people can go from one to the other. They start by being ‘at risk’ of radicalization, but if they do get radicalized and become a violent threat to society, which only happens in the minority of cases, then they become ‘a risk’. Engaging with people who are ‘at risk’ highlights the importance of having respect and dialogue with communities that are often the first to be lambasted when things go wrong, but which seldom get all the help they need, or the credit when they get it right. We argue that civil society is particularly suited for being part of this process.

Ed: It seems like the things that people do or say online can only really be understood in terms of the context. But often we don’t have enough information, and it can be very hard to just look at something and say ‘This is definitely extremist material that is going to incite someone to commit terrorist or violent acts’.

Josh: Yes, I think you’re right. In the report we try to take what is a very complicated concept – extremist material – and divide it into more manageable chunks of meaning. We talk about three hierarchical levels. The degree of legal consensus over whether content should be banned decreases as it gets less extreme. The first level we identified was straight up provocation and hate speech. Hate speech legislation has been part of the law for a long time. You can’t incite racial hatred, you can’t incite people to crimes, and you can’t promote terrorism. Most countries in Europe have laws against these things.

The second level is the glorification and justification of terrorism. This is usually more post-hoc as by definition if you are glorifying something it has already happened. You may well be inspiring future actions, but that relationship between the act of violence and the speech act is different than with provocation. Nevertheless, some countries, such as Spain and France, have pushed hard on criminalising this. The third level is non-violent extremist material. This is the most contentious level, as there is very little consensus about what types of material should be called ‘extremist’ even though they are non-violent. One of the interviewees that we spoke to said that often it is hard to distinguish between someone who is just being friendly and someone who is really trying to persuade or groom someone to go to Syria. It is really hard to put this into a legal framework with the level of clarity that the law demands.

There is a proportionality question here. When should something be considered specifically illegal? And, then, if an illegal act has been committed what should the appropriate response be? This is bound to be very different in different situations.

Ed: Do you think that there are any immediate or practical steps that governments can take to improve the current situation? And do you think that there any ethical concerns which are not being paid sufficient attention?

Josh: In the report we raised a few concerns about existing government responses. There are lots of things beside privacy that could be seen as fundamental human rights and that are being encroached upon. Freedom of association and assembly is a really interesting one. We might not have the same reverence for a Facebook event plan or discussion group as we would a protest in a town hall, but of course they are fundamentally pretty similar.

The wider danger here is the issue of mission creep. Once you have systems in place that can do potentially very powerful analytical investigatory things then there is a risk that we could just keep extending them. If something can help us fight terrorism then should we use it to fight drug trafficking and violent crime more generally? It feels to me like there is a technical-military-industrial complex mentality in government where if you build the systems then you just want to use them. In the same way that CCTV cameras record you irrespective of whether or not you commit a violent crime or shoplift, we need to ask whether the same panoptical systems of surveillance should be extended to the Internet. Now, to a large extent they are already there. But what should we train the torchlight on next?

This takes us back to the importance of having necessary, proportionate, and independently authorized processes. When you drill down into how rights privacy should be balanced with security then it gets really complicated. But the basic process-driven things that we identified in the report are far simpler: if we accept that governments have the right to take certain actions in the name of security, then, no matter how important or life-saving those actions are, there are still protocols that governments must follow. We really wanted to infuse these issues into the debate through the report.

Read the full report: Brown, I., and Cowls, J., (2015) Check the Web: Assessing the Ethics and Politics of Policing the Internet for Extremist Material. VOX-Pol Publications.

Josh Cowls is a a student and researcher based at MIT, working to understand the impact of technology on politics, communication and the media.

Josh Cowls was talking to Blog Editor Bertie Vidgen.

Collecting, storing and distributing digital data is significantly easier and cheaper now than ever before, in line with predictions from Moore, Kryder and Gilder. Organisations are incentivised to collect large volumes of data with the hope of unleashing new business opportunities or maybe even new businesses. Consider the likes of uber, Netflix, and Airbnb and the other data mongers who have built services based solely on digital assets.

The use of this new abundant data will continue to disrupt traditional business models for years to come, and there is no doubt that these large data volumes can provide value. However, they also bring associated risks (such as unplanned disclosure and hacks) and they come with constraints (for example in the form of privacy or data protection legislation). Hardly a week goes by without a data breach hitting the headlines. Even if your telecommunications provider didn’t inadvertently share your bank account and sort code with hackers, and your child wasn’t one of the hundreds of thousands of children whose birthdays, names, and photos were exposed by a smart toy company, you might still be wondering exactly how your data is being looked after by the banks, schools, clinics, utility companies, local authorities and government departments that are so quick to collect your digital details.

Then there are the companies who have invited you to sign away the rights to your data and possibly your privacy too – the ones that ask you to sign the Terms and Conditions for access to a particular service (such as a music or online shopping service) or have asked you for access to your photos. And possibly you are one of the “worried well” who wear or carry a device that collects your health data and sends it back to storage in a faraway country, for analysis.

So unless you live in a lead-lined concrete bunker without any access to internet connected devices, and you don’t have the need to pass by webcams or sensors, or use public transport or public services; then your data is being collected and shared. And for the majority of the time, you benefit from this enormously. The bus stop tells you exactly when the next bus is coming, you have easy access to services and entertainment fitted very well to your needs, and you can do most of your bank and utility transactions online in the peace and quiet of your own home. Beyond you as an individual, there are organisations “out there” sharing your data to provide you better healthcare, education, smarter city services and secure and efficient financial services, and generally matching the demand for services with the people needing them.

So we most likely all have data that is being shared and it is generally in our interest to share it, but how can we trust the organisations responsible for sharing our data? As an organisation, how can I know that my partner and supplier organisations are taking care of my client and product information?

Organisations taking these issues seriously are coming up with their own internal policy and guidelines. However, for data sharing between organisations to be straight forward, there needs to a common understanding of basic policy and practice. During my time as a visiting associate at the Oxford Internet Institute, University of Oxford, I have developed a pragmatic solution in the form of a Voluntary Code. The Code has been produced using the guidelines for voluntary code development produced by the Office of Community Affairs, Industry Canada. More importantly, the Code is anchored in the developing ISO standards for the Governance of Data (the 38505 series). These standards apply the governance principles and model from the 38500 standard and introduce the concept of a data accountability map, highlighting six focus areas for a governing body to apply governance. The early stage standard suggests considering the aspects of Value, Risk and Constraint for each area, to determine what practice and policy should be applied to maximise the value from organisational data, whilst applying constraints as set by legislation and local policy, and minimising risk.

I am Head of the New Zealand delegation to the ISO group developing IT Service Management and IT Governance standards, SC40, and am leading the development of the 38505 series of Governance of Data standards, working with a talented editorial team of industry and standards experts from Australia, China and the Netherlands. I am confident that the robust ISO consensus-led process involving subject matter experts from around the world, will result in the publication of best practice guidance for the governance of data, presented in a format that will have relevance and acceptance internationally.

In the meantime, however, I see a need to provide urgent advice to organisations struggling with policy for sharing data. I have used my time at Oxford to interview policy, ethics, smart city, open data, health informatics, education, cyber security and social science experts and users, owners and curators of large data sets, and have come up with a “Voluntary Code for Data Sharing”. The Code takes three areas from the data accountability map in the developing ISO standard 38505-1; namely Collect, Store, Distribute, and applies the aspects of Value, Risk and Constraint to provide seven maxims for sharing data. To assist with adoption and compliance, the Code provides references to best practice and examples. As the ISO standards for the Governance of Data develop, the Code will be updated. New examples of good practice will be added as they come to light.

[A permanent home for the voluntary code is currently being organised; please email me in the meantime if you are interested in it: Alison.holt@longitude174.com]

The Code is deliberately short and succinct, but it does provide links for those who need to read more to understand the underpinning practices and standards, and those tasked with implementing organisational data policy and practice. It cannot guarantee good outcomes. With new security threats arising daily, nobody can fully guarantee the safety of your information. However, if you deal with an organisation that is compliant with the Voluntary Code, then at least you can have assurance that the organisation has at least considered how it is using your data now and how it might want to reuse your data in the future, how and where your data will be stored, and then finally how your data will be distributed or discarded. And that’s a good start!

Alison Holt was an OII Academic Visitor in late 2015. She is an internationally acclaimed expert in the Governance of Information Technology and Data, heading up the New Zealand delegations to the international standards committees for IT Governance and Service Management (SC40) and Software and Systems Engineering (SC7). The British Computer Society published Alison’s first book on the Governance of IT in 2013.

To some extent the citizen-based response filled the gap left by the lack of a sufficient response from the traditional institutions.[1] This suggests that the role of ICTs in disaster response should be examined within the political context of the power relationship between members of the public who use digital tools and the traditional institutions. My experience in 2010 was the first time I was able to see that, while we would expect that in a case of natural disaster both the authorities and the citizens would be mostly concerned about the emergency, the actual situation might be different.

Apparently the emergence of independent, citizen-based collective action in response to a disaster was considered as some type of threat by the institutional actors. First, it was a threat to the image of these institutions, which didn’t want citizens to be portrayed as the leading responding actors. Second, any type of citizen-based collective action, even if not purely political, may be an issue of concern in authoritarian countries in particular. Accordingly, one can argue that, while citizens are struggling against a disaster, in some cases the traditional institutions may make substantial efforts to restrain and contain the action of citizens. In this light, the role of information technologies can include not only enhancing citizen engagement and increasing the efficiency of the response, but also controlling the digital crowd of potential volunteers.

The purpose of this paper was to conceptualize the tension between the role of ICTs in the engagement of the crowd and its resources, and the role of ICTs in controlling the resources of the crowd. The research suggests a theoretical and methodological framework that allows us to explore this tension. The paper focuses on an analysis of specific platforms and suggests empirical data about the structure of the platforms, and interviews with developers and administrators of the platforms. This data is used in order to identify how tools of engagement are transformed into tools of control, and what major differences there are between platforms that seek to achieve these two goals. That said, obviously any platform can have properties of control and properties of engagement at the same time; however the proportion of these two types of elements can differ significantly.

One of the core issues for my research is how traditional actors respond to fast, bottom-up innovation by citizens.[2]. On the one hand, the authorities try to restrict the empowerment of citizens by the new tools. On the other hand, the institutional actors also seek to innovate and develop new tools that can restore the balance of power that has been challenged by citizen-based innovation. The tension between using digital tools for the engagement of the crowd and for control of the crowd can be considered as one of the aspects of this dynamic.

That doesn’t mean that all state-backed platforms are created solely for the purpose of control. One can argue, however, that the development of digital tools that offer a mechanism of command and control over the resources of the crowd is prevalent among the projects that are supported by the authorities. This can also be approached as a means of using information technologies in order to include the digital crowd within the “vertical of power”, which is a top-down strategy of governance. That is why this paper seeks to conceptualize this phenomenon as “vertical crowdsourcing”.

The question of whether using a digital tool as a mechanism of control is intentional is to some extent secondary. What is important is that the analysis of platform structures relying on activity theory identifies a number of properties that allow us to argue that these tools are primarily tools of control. The conceptual framework introduced in the paper is used in order to follow the transformation of tools for the engagement of the crowd into tools of control over the crowd. That said, some of the interviews with the developers and administrators of the platforms may suggest the intentional nature of the development of tools of control, while crowd engagement is secondary.

[1] Asmolov G. “Natural Disasters and Alternative Modes of Governance: The Role of Social Networks and Crowdsourcing Platforms in Russia”, in Bits and Atoms Information and Communication Technology in Areas of Limited Statehood, edited by Steven Livingston and Gregor Walter-Drop, Oxford University Press, 2013.

[2] Asmolov G., “Dynamics of innovation and the balance of power in Russia”, in State Power 2.0 Authoritarian Entrenchment and Political Engagement Worldwide, edited by Muzammil M. Hussain and Philip N. Howard, Ashgate, 2013.

Read the full article: Asmolov, G. (2015) Vertical Crowdsourcing in Russia: Balancing Governance of Crowds and State–Citizen Partnership in Emergency Situations. Policy and Internet 7,3: 292–318.

Gregory Asmolov is a PhD student at the LSE, where he is studying crowdsourcing and emergence of spontaneous order in situations of limited statehood. He is examining the emerging collaborative power of ICT-enabled crowds in crisis situations, and aiming to investigate the topic drawing on evolutionary theories concerned with spontaneous action and the sustainability of voluntary networked organizations. He analyzes whether crowdsourcing practices can lead to development of bottom-up online networked institutions and “peer-to-peer” governance.

Australia’s new data retention law for telecommunications providers, comparable to extant UK and US legislation, came into effect 13 October 2015. Telecoms and ISPs are now required to retain metadata about communications for two years to assist law enforcement agencies in crime and terrorism investigation. Despite now being in effect, the extent and types of data to be collected remain unclear. The law has been widely criticised for violating Australians’ right to privacy by introducing overly broad surveillance of civilians. The Government has argued against this portrayal. They argue the content of communications will not be retained but rather the “data about the data” – location, time, date and duration of a call.

Metadata retention raises complex ethical issues often framed in terms of privacy which are relevant globally. A popular argument is that metadata offers a lower risk of violating privacy compared to primary data – the content of communication. The distinction between the “content” and “nature” of a communication implies that if the content of a message is protected, so is the privacy of the sender and receiver.

The assumption that metadata retention is more acceptable because of its lower privacy risks is unfortunately misguided. Sufficient volumes of metadata offer comparable opportunities to generate invasive information about civilians. Consider a hypothetical. I am given access to a mobile carrier’s dataset that specifies time, date, caller and receiver identity in addition to a continuous record of location constructed with telecommunication tower triangulation records. I see from this that when John’s wife Jane leaves the house, John often calls Jill and visits her for a short period from afterwards. From this I conclude that John may be having an affair with Jill. Now consider the alternative. Instead of metadata I have access to recordings of the calls between John and Jill with which I reach the same conclusion.

From a privacy perspective the method I used to infer something about John’s marriage is trivial. In both cases I am making an intrusive inference about John based on data that describes his behaviours. I cannot be certain but in both cases I am sufficiently confident that my inference is correct based on the data available. My inferences are actionable – I treat them as if they are reliable, accurate knowledge when interacting with John. It is this willingness to act on uncertainty (which is central to ‘Big Data’) that makes metadata ethically similar to primary data. While it is comparatively difficult to learn something from metadata, the potential is undeniable. Both types allow for invasive inferences to be made about the lives and behaviours of people.

Going further, some would argue that metadata can actually be more invasive than primary data. Variables such as location, time and duration are easier to assemble into a historical record of behaviour than content. These concerns are deepened by the difficulty of “opting out” of metadata surveillance. While a person can hypothetically forego all modern communication technologies, privacy suddenly has a much higher cost in terms of quality of life.

Technologies such as encrypted communication platforms, virtual private networks (VPN) and anonymity networks have all been advocated as ways to subvert metadata collection by hiding aspects of your communications. It is worth remembering that these techniques remain feasible only so long as they remain legal, one has the technical knowledge and (in some cases) ability to pay. These technologies raise a question of whether a right to anonymity exists. Perhaps privacy enhancing technologies are immoral? Headlines about digital piracy and the “dark web” show how quickly technologically hiding one’s identity and behaviours can take on a criminal and immoral tone. The status quo of privacy subtly shifts when techniques to hide aspects of one’s personal life are portrayed as necessarily subversive. The technologies to combat metadata retention are not criminal or immoral – they are privacy enhancing technologies.

Privacy is historically a fundamental human value. Individuals have a right to privacy. Violations must be justified by a competing interest. In discussing the ethics of metadata retention and anonymity technologies it is easy to forget this status quo. Privacy is not something that individuals have to justify or argue for – it should be assumed.

Brent Mittelstadt is a Postdoctoral Research Fellow at the Oxford Internet Institute working on the ‘Ethics of Biomedical Big Data‘ project with Prof. Luciano Floridi. His research interests include the ethics of information handled by medical ICT, theoretical developments in discourse and virtue ethics, and epistemology of information.

Crowdsourcing is a recent buzzword that describes efforts to use the Internet to mobilize online communities to achieve specific organizational goals. While crowdsourcing serves several purposes, the most interesting potential from a democratic perspective is the ability to crowdsource legislation. By giving citizens the means to affect the legislative process more directly, crowdsourcing legislation is an example of a democratic innovation that gives citizens a say in the legislative process. Recent years have witnessed a scholarly debate on whether such new forms of participatory governance can help cure democratic deficits such as a declining political legitimacy of the political system in the eyes of the citizenry. However, it is still not clear how taking part in crowdsourcing affects the political attitudes of the participants, and the potential impact of such democratic innovations therefore remain unclear.

In our study, we contribute to this research agenda by exploring how crowdsourcing citizens’ initiatives affected political attitudes in Finland. The non-binding Citizens’ Initiative instrument in Finland was introduced in spring 2012 to give citizens the chance to influence the agenda of the political decision making. In particular, we zoom in on people active on the Internet website Avoin Ministeriö (Open Ministry), which is a site based on the idea of crowdsourcing where users can draft citizens’ initiatives and deliberate on their contents. As is frequently the case for studies of crowdsourcing, we find that only a small portion of the users are actively involved in the crowdsourcing process. The option to deliberate on the website was used by about 7% of the users; the rest were only passive readers or supported initiatives made by others. Nevertheless, Avoin Ministeriö has been instrumental in creating support for several of the most successful initiatives during the period, showing that the website has been a key actor during the introductory phase of the Citizens’ initiative in Finland.

We study how developments in political attitudes were affected by outcome satisfaction and process satisfaction. Outcome satisfaction concerns whether the participants get their preferred outcome through their involvement, and this has been emphasized by proponents of direct democracy. Since citizens get involved to achieve a specific outcome, their evaluation of the experience hinges on whether or not they achieve this outcome. Process satisfaction, on the other hand, is more concerned with the perceived quality of decision making. According to this perspective, what matters is that participants find that their concerns are given due consideration. When people find the decision making to be fair and balanced, they may even accept not getting their preferred outcome. The relative importance of these two perspectives remains disputed in the literature.

The research design consisted of two surveys administered to the users of Avoin Ministeriö before and after the decision of the Finnish Parliament on the first citizens’ initiative in concerning a ban on the fur-farming industry in Finland. This allowed us to observe how involvement in the crowdsourcing process shaped developments in central political attitudes among the users of Avoin Ministeriö and what factors determined the developments in subjective political legitimacy. The first survey was conducted in fall 2012, when the initiators were gathering signatures in support of the initiative to ban fur-farming, while the second survey was conducted in summer 2013 when Parliament rejected the initiative. Altogether 421 persons filled in both surveys, and thus comprised the sample for the analyses.

The study yielded a number of interesting findings. First of all, those who were dissatisfied with Parliament rejecting the initiative experienced a significantly more negative development in political trust compared to those who did not explicitly support the initiative. This shows that the crowdsourcing process had a negative impact on political legitimacy among the initiative’s supporters, which is in line with previous contributions emphasizing the importance of outcome legitimacy. It is worth noting that this also affected trust in the Finnish President, even if he has no formal powers in relation to the Citizens’ initiative in Finland. This shows that negative effects on political legitimacy could be more severe than just a temporary dissatisfaction with the political actors responsible for the decision.

Nevertheless, the outcome may not be the most important factor for determining developments in political legitimacy. Our second major finding indicated that those who were dissatisfied with the way Parliament handled the initiative also experienced more negative developments in political legitimacy compared to those who were satisfied. Furthermore, this effect was more pervasive than the effect for outcome satisfaction. This implies that the procedures for handling non-binding initiatives may play a strong role in citizens’ perceptions of representative institutions, which is in line with previous findings emphasising the importance of procedural aspects and evaluations for judging political authorities.

We conclude that there is a beneficial impact on political legitimacy if crowdsourced citizens’ initiatives have broad appeal so they can be passed in Parliament. However, it is important to note that positive effects on political legitimacy do not hinge on Parliament approving citizens’ initiatives. If the MPs invest time and resources in the careful, transparent and publicly justified handling of initiatives, possible negative effects of rejecting initiatives can be diminished. Citizens and activists may accept an unfavourable decision if the procedure by which it was reached seems fair and just. Finally, the results give reason to be hopeful about the role of crowdsourcing in restoring political legitimacy, since a majority of our respondents felt that the possibility of crowdsourcing citizens’ initiatives clearly improved Finnish democracy.

While all hopes may not have been fulfilled so far, crowdsourcing legislation therefore still has potential to help rebuild political legitimacy.

Read the full article: Christensen, H., Karjalainen, M., and Nurminen, L., (2015) Does Crowdsourcing Legislation Increase Political Legitimacy? The Case of Avoin Ministeriö in Finland. Policy and Internet 7 (1) 25–45.

Henrik Serup Christensen is Academy Research Fellow at SAMFORSK, Åbo Akademi University.

Maija Karjalainen is a PhD Candidate at the Department of Political Science and Contemporary History in the University of Turku, Finland.

Laura Nurminen is a Doctoral Candidate at the Department of Political and Economic Studies at Helsinki University, Finland.

Ed: There is much discussion about a perceived “legitimacy crisis” in democracy. For example, less than half of the Finnish electorate under 40 choose to vote. In your article you argue that Finland’s 2012 Citizens’ Initiative Act aimed to address this problem by allowing for the crowdsourcing of ideas for new legislation. How common is this idea? (And indeed, how successful?)

Taneli: The idea that digital participation could counter the “legitimacy crisis” is a fairly common one. Digital utopians have nurtured that idea from the early years of the internet, and have often been disappointed. A couple of things stand out in the Finnish experiment that make it worth a closer look.

First, the digital crowdsourcing system with strong digital identification is a reliable and potentially viral campaigning tool. Most civic initiative systems I have encountered rely on manual or otherwise cumbersome, and less reliable, signature collection methods.

Second, in the Finnish model, initiatives that break the threshold of 50,000 names must be treated in the Parliament equally to an initiative from a group of MPs. This gives the initiative constitutional and political weight.

Ed: The Act led to the passage of Finland’s first equal marriage law in 2014. In this case, online platforms were created for collecting signatures as well as drafting legislation. An NGO created a well-used platform, but it subsequently had to shut it down because it couldn’t afford the electronic signature system. Crowds are great, but not a silver bullet if something as prosaic as authentication is impossible. Where should the balance lie between NGOs and centrally funded services, i.e. government?

Taneli: The crucial thing in the success of a civic initiative system is whether it gives the people real power. This question is decided by the legal framework and constitutional basis of the initiative system. So, governments have a very important role in this early stage – designing a law for truly effective citizen initiatives.

When a framework for power-making is in place, service providers will emerge. Should the providers be public, private or third sector entities? I think that is defined by local political culture and history.

In the United States, the civic technology field is heavily funded by philanthropic foundations. There is an urge to make these tools commercially viable, though no one seems to have figured out the business model. In Europe there’s less philanthropic money, and in my experience experiments are more often government funded.

Both models have their pros and cons, but I’d like to see the two continents learning more from each other. American digital civic activists tell me enviously that the radically empowering Finnish model with a government-run service for crowdsourcing for law would be impossible in the US. In Europe, civic technologists say they wish they had the big foundations that Americans have.

Ed: But realistically, how useful is the input of non-lawyers in (technical) legislation drafting? And is there a critical threshold of people necessary to draft legislation?

Taneli: I believe that input is valuable from anyone who cares to invest some time in learning an issue. That said, having lawyers in the campaign team really helps. Writing legislation is a special skill. It’s a pity that the co-creation features in Finland’s Open Ministry website were shut down due to a lack of funding. In that model, help from lawyers could have been made more accessible for all campaign teams.

In terms of numbers, I don’t think the size of the group is an issue either way. A small group of skilled and committed people can do a lot in the drafting phase.

Ed: But can the drafting process become rather burdensome for contributors, given professional legislators will likely heavily rework, or even scrap, the text?

Taneli: Professional legislators will most likely rework the draft, and that is exactly what they are supposed to do. Initiating an idea, working on a draft, and collecting support for it are just phases in a complex process that continues in the parliament after the threshold of 50,000 signatures is reached. A well-written draft will make the legislators’ job easier, but it won’t replace them.

Ed: Do you think there’s a danger that crowdsourcing legislation might just end up reflecting the societal concerns of the web-savvy – or of campaigning and lobbying groups

Taneli: That’s certainly a risk, but so far there is little evidence of it happening. The only initiative passed so far in Finland – the Equal Marriage Act – was supported by the majority of Finns and by the majority of political parties, too. The initiative system was used to bypass a political gridlock. The handful of initiatives that have reached the 50,000 signatures threshold and entered parliamentary proceedings represent a healthy variety of issues in the fields of education, crime and punishment, and health care. Most initiatives seem to echo the viewpoint of the ‘ordinary people’ instead of lobbies or traditional political and business interest groups.

Ed: You state in your article that the real-time nature of digital crowdsourcing appeals to a generation that likes and dislikes quickly; a generation that inhabits “the space of flows”. Is this a potential source of instability or chaos? And how can this rapid turnover of attention be harnessed efficiently so as to usefully contribute to a stable and democratic society?

Taneli: The Citizens’ Initiative Act in Finland is one fairly successful model to look at in terms of balancing stability and disruptive change. It is a radical law in its potential to empower the individual and affect real power-making. But it is by no means a shortcut to ‘legislation by a digital mob’, or anything of that sort. While the digital campaigning phase can be an explosive expression of the power of the people in the ‘time and space of flows’, the elected representatives retain the final say. Passing a law is still a tedious process, and often for good reasons.

Ed: You also write about the emergence of the “mediating citizen” – what do you mean by this?

Taneli: The starting point for developing the idea of the mediating citizen is Lance Bennett’s AC/DC theory, i.e. the dichotomy of the actualising and the dutiful citizen. The dutiful citizen is the traditional form of democratic citizenship – it values voting, following the mass media, and political parties. The actualising citizen, on the other hand, finds voting and parties less appealing, and prefers more flexible and individualised forms of political action, such as ad hoc campaigns and the use of interactive technology.

I find these models accurate but was not able to place in this duality the emerging typologies of civic action I observed in the Finnish case. What we see is understanding and respect for parliamentary institutions and their power, but also strong faith in one’s skills and capability to improve the system in creative, technologically savvy ways. I used the concept of the mediating citizen to describe an actor who is able to move between the previous typologies, mediating between them. In the Finnish example, creative tools were developed to feed initiatives in the traditional power-making system of the parliament.

Ed: Do you think Finland’s Citizens Initiative Act is a model for other governments to follow when addressing concerns about “democratic legitimacy”?

Taneli: It is an interesting model to look at. But unfortunately the ‘legitimacy crisis’ is probably too complex a problem to be solved by a single participation tool. What I’d really like to see is a wave of experimentation, both on-line and off-line, as well as cross-border learning from each other. And is that not what happened when the representative model spread, too?

Read the full article: Heikka, T., (2015) The Rise of the Mediating Citizen: Time, Space, and Citizenship in the Crowdsourcing of Finnish Legislation. Policy and Internet 7 (3) 268–291.

Taneli Heikka is a journalist, author, entrepreneur, and PhD student based in Washington.

Taneli Heikka was talking to Blog Editor Pamina Smith.

Simo: Over the years we’ve deployed various types of interactive interfaces on a network of public displays. In this case it was basically a network of interactive screens deployed in downtown Oulu, next to where a renovation project was happening that we wanted to collect feedback about. We deployed an app on the screens, that allowed people to type feedback direcly on the screens (on-screen soft keyboard), and submit feedback to city authorities via SMS, Twitter and email. We also had a smiley-based “rating” system there, which people could us to leave quick feedback about certain aspects of the renovation project.

We ourselves could not, and did not even want to, assess the impact — that’s why we did this in partnership with the city authorities. Then, together with the city folks we could better evaluate if what we were doing had any real-world value whatsoever. And, as we discuss, in the end it did!

Ed: How did you go about encouraging citizens to engage with touch screen technologies in a public space — particularly the non-digitally literate, or maybe people who are just a bit shy about participating?

Simo: Actually, the whole point was that we did not deliberately encourage them by advertising the deployment or by “forcing” anyone to use it. Quite to the contrary: we wanted to see if people voluntarily used it, and the technologies that are an integral part of the city itself. This is kind of the future vision of urban computing, anyway. The screens had been there for years already, and what we wanted to see is if people find this type of service on their own when exploring the screens, and if they take the opportunity to then give feedback using them. The screens hosted a variety of other applications as well: games, news, etc., so it was interesting to also gauge how appealing the idea of public civic feedback is in comparison to everything else that was being offered.

Ed: You mention that using SMS to provide citizen feedback was effective in filtering out noise since it required a minimal payment from citizens — but it also created an initial barrier to participation. How do you increase the quality of feedback without placing citizens on different-level playing fields from the outset — particularly where technology is concerned?

Simo: Yes, SMS really worked well in lowering the amount of irrelevant commentary and complete nonsense. And it is true that SMS already introduces a cost, and even if the cost is miniscule, it’s still a cost to the citizen — and just voicing one’s opinions should of course be free. So there’s no correct answer here — if the channel is public and publicly accessible to anyone, there will be a lot of noisy input. In such cases moderation is a heavy task, and to this end we have been exploring crowdsourcing as well. We can make the community moderate itself. First, we need to identify the users who are genuinely concerned or interested about the issues being explored, and then funnel those users to moderate the discussion / output. It is a win-win situation — the people who want to get involved are empowered to moderate the commentary from others, for implicit rewards.

Ed: For this experiment on citizen feedback in an urban space, your team assembled the world’s largest public display network, which was available for research purposes 24/7. In deploying this valuable research tool, how did you guarantee the privacy of the participants involved, given that some might not want to be seen submitting very negative comments? (e.g. might a form of social pressure be the cause of relatively low participation in the study?)

Simo: The display network was not built only for this experiment, but we have run hundreds of experiments on it, and have written close to a hundred academic papers about them. So, the overarching research focus, really, is on how we can benefit citizens using the network. Over the years we have been able to systematically study issues such as social pressure, group use, effects of the public space, or, one might say “stage”, etc. And yes, social pressure does affect a lot, and for this allowing people participate via e.g. SMS or email helps a lot. That way the users won’t be seen sending the input directly.

Group use is another thing: in groups people don’t feel pressure from the “outside world” so much and are willing to interact with our applications (such as the one documented in this work), but, again, it affects the feedback quality. Groups don’t necessarily tell the truth as they aim for consensus. So the individual, and very important, opinions may not become heard. Ultimately, this is all just part of the game we must deal with, and the real question becomes how to minimize those negative effects that the public space introduces. The positives are clear: everyone can participate, easily, in the heart of the city, and whenever they want.

Ed: Despite the low participation, you still believe that the experimental results are valuable. What did you learn?

Simo: The question in a way already reveals the first important point: people are just not as interested in these “civic” things as they might claim in interviews and pre-studies. When we deploy a civic feedback prototype as the “only option” on a public gizmo (a display, some kind of new tech piece, etc.), people out of curiosity use it. Now, in our case, we just deploy it “as is”, as part of the city infrastructure for people to use if, and only if, they want to use it. So, the prototype competes for attention against smartphones, other applications on the displays, the cluttered city itself… everything!

When one reads many academic papers on interactive civic engagement prototypes, the assumptions are set very high in the discussion: “we got this much participation in this short time”, etc., but that’s not the entire truth. Leave the thing there for months and see if it still interests people! We have done the same, deployed a prototype for three days, gotten tons of interaction, published it, and learned only afterwards that “oh, maybe we were a bit optimistic with the efficiency” when the use suddenly dropped to minimum. It’s just not that easy and the application require frequent updates to keep user interest longitudinally.

Also, the radical differences in the feedback channels were surprising, but we already talked about that a bit earlier.

Ed: Your team collaborated with local officials, which is obviously valuable (and laudable), but it can potentially impose an extra burden on academics. For example, you mention that instead of employing novel feedback formats (e.g. video, audio, images, interactive maps), your team used only text. But do you think working with public officials benefitted the project as a whole, and how?

Simo: The extra burden is a necessity if one wants to really claim authentic success in civic engagement. In our opinion, it only happens between citizens and the city, not between citizens and researchers. We do not wish to build these deployments for the sake of an academic article or two: the display infrastructure is there for citizens and the city, and if we don’t educate the authorities on how to use it then nobody will. Advertisers would be glad to take over the entire real estate there, so in a way this project is just a part of the bigger picture. Which is making the display infrastructure “useful” instead of just a gimmick to kill time with (games) or for advertising.

And yes, the burden is real, but also because of this we could document what we have learned about dealing with authorities: how it is first easy to sell these prototypes to them, but sometimes hard to get commitment, etc. And it is not just this prototype — we’ve done a number of other civic engagement projects where we have noticed the same issues mentioned in the paper as well.

Ed: You also mention that as academics and policymakers you had different notions of success: for example in terms of levels of engagement and feedback of citizens. What should academics aspiring to have a concrete impact on society keep in mind when working with policymakers?

Simo: It takes a lot of time to assess impact. Policymakers will not be able to say after only a few weeks (which is the typical length of studies in our field) if the prototype has actual value to it, or if it’s just a “nice experiment”. So, deploy your strategy / tech / anything you’re doing, write about it, and let it sit. Move on with the life, and then revisit it after months to see if anything has come out of it! Patience is key here.

Ed: Did the citizen feedback result in any changes to the urban renovation project they were being consulted on?

Simo: Not the project directly: the project naturally was planned years ahead and the blueprints were final at that point. The most remarkable finding for us (and the authorities) was that after moderating the noise out from the feedback, the remaining insight was pretty much the only feedback that they ever directly got from citizens. Finns tend to be a bit on the shy side, so people won’t just pick up the phone and call the local engineering department and speak out. Not sure if anyone does, really? So they complain and chat on forums and coffee tables. So it would require active work for the authorities to find and reach out to these people.

With the display infrastructure, which was already there, we were able to gauge the public opinion that did not affect the construction directly, but indirectly affected how the department could manage their press releases, which things to stress in public communications, what parts of PR to handle differently in the next stage of the renovation project etc.

Ed: Are you planning any more experiments?

Simo: We are constantly running quite a few experiments. On the civic engagement side, for example, we are investigating how to gamify environmental awareness (recycling, waste management, keeping the environment clean) for children, as well as running longer longitudinal studies to assess the engagement of specify groups of people (e.g., children and the elderly).

Read the full article: Hosio, S., Goncalves, J., Kostakos, V. and Riekki, J. (2015) Crowdsourcing Public Opinion Using Urban Pervasive Technologies: Lessons From Real-Life Experiments in Oulu. Policy and Internet 7 (2) 203–222.

Simon Hosio is a research scientist (Dr. Tech.) at the University of Oulu, in Finland. Core topics of his research are smart city tech, crowdsourcing, wisdom of the crowd, civic engagement, and all types of “mobile stuff” in general.

Simo Hosio was talking to blog editor Pamina Smith.

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!

To maintain an open and working Internet, we need to make sense of how the complex and decentralised technical system operates. Research groups, governments, and companies have dedicated teams working on highly technical research and experimentation to make sense of information flows and how these can be affected by new developments, be they intentional or due to unforeseen consequences of decisions made in another domain.

These teams, composed of network engineers and computer scientists, therefore analyse Internet data transfers, typically by collecting data from devices of large groups of individuals as well as organisations. The Internet, however, has become a complex and global socio-technical information system that mediates a significant amount of our social or professional activities, relationships, as well as mental processes. Experimentation and research on the Internet therefore require ethical scrutiny in order to give useful feedback to engineers and researchers about the social impact of their work.

The organising committee of the Association of Computing Machinery (ACM) SigComm (Signal Communications) conference has regularly encountered paper submissions that can be considered dubious from an ethical point of view. A strong debate on the research ethics of the ACM was sparked by the paper entitled “Encore: Lightweight Measurement of Web Censorship with Cross-Origin Requests,” among others submitted for the 2015 conference. In the study, researchers directed unsuspecting Internet users to test potential censorship systems in their country by directing their browser to specified URLs that could be blocked in their jurisdiction. Concerns were raised about whether this could be considered ‘human subject research’ and whether the unsuspecting users could be harmed as a result of this experiment. Consider, for example, a Chinese citizen continuously requesting the Falun Gong website from their Beijing-based laptop with no knowledge of this occurring whatsoever.

As a result of these discussions, the ACM realised that there was no formal procedure or methodology in place to make informed decisions about the ethical dimensions of such research. The conference therefore hosted a one-day workshop led by the OII’s Ethics in Networked Systems Research (ENSR) project. The day brought together 55 participants from different academic disciplines, ranging from computer science to philosophy, law, sociology, and social science. As part of a broader mission to establish ethical guidelines for Internet research, the aim of the workshop was to inform participants about the pressing ethical issues of the network measurement discipline, and to exchange ideas, reasoning, and proposed solutions.

The workshop began with two interactive sessions in which participants split into small, multidisciplinary groups to debate the submitted papers. Participants recorded their thoughts on key issues that emerged in the discussions. The remaining sessions of the day concentrated on the main themes surfacing from these notes as well as the active feedback of attendees. In this manner, participants from both sides of the debate — that is, the technical researchers and the non-technical researchers — were able to continually quiz each other about the strengths and weaknesses of their approach. The workshop’s emphasis on collaboration across academic disciplines, thereby creating an interdisciplinary community of researchers interested in Internet ethics, aimed to create a more solid foundation for building functional ethical standards in this area.

The interactive discussions yielded some particularly interesting recommendations regarding both the general ethical governance of computer science research as well as particular pressing issues. The main suggestion of the workshop was to create a procedure for an iterative approach to ethical review, whereby the relevant authority (e.g. conference programme committee, institutional ethics board, journal editor, funding agencies) and the researchers could engage in a dialogue about the impact of research, rather than have these restricted by a top-down, one-time decision of the authority.

This approach could be supported by the guidelines that the OII’s ENSR project is currently drafting. Further, participants explored to what extent computer ethics can be taught as part of every module of computer science degrees, rather than the current generic ethics courses generally taught to engineering students. This adjustment would thereby allow aspiring technical researchers to develop a hands-on sense of the social and ethical implications of new technologies and methodologies. Participants agreed that this idea would take an intensive department-wide effort, but would be very worthwhile in the end.

In more practical discussions, participants exchanged views on a wide range of potential solutions or approaches to ethical issues resulting from Internet research. For example, technical researchers struggling with obtaining  informed consent were advised to focus their efforts on user-risk mitigation (with many nuances that exceed this blog post). For those studying the Internet in foreign countries, participants recommended running a few probes with the proposed methodology. This exploratory study would then serve to underpin an informed discussion on the possible social implications of the project with organizations and researchers who are more knowledgeable of the local context (e.g. anthropologists, sociologists or NGOs, among others).

Other concrete measures proposed to improve academic research included: fictionalizing rejected case studies to help researchers understand reasons for rejection without creating a ‘hall of shame’; generating a list of basic ethical questions that all papers should answer in the proposal phase; and starting a dialogue with other research communities in analogous situations concerning ethics.

The workshop comprised some high-level discussions to get participants on the same page, and deep dives into specific topics to generate some concrete solutions. As participants wrote down their thoughts on post-it notes, the next steps will be to categorise these notes, develop initial draft guidelines, and discuss these with all participants on the dedicated mailing list.

If you would like to join this mailing list, please e-mail bendert.zevenbergen [at] oii.ox.ac.uk! More detailed write-ups of the workshop outcomes will be published in due course.

Ben Zevenbergen is a student at the Oxford Internet Institute pursuing a DPhil on the intersection of privacy law, technology, social science, and the Internet. He runs a side project that aims to establish ethics guidelines for Internet research, as well as working in multidisciplinary teams such as the EU funded Network of Excellence in Internet Science. He has worked on legal, political and policy aspects of the information society for several years. Most recently he was a policy advisor to an MEP in the European Parliament, working on Europe’s Digital Agenda. Previously Ben worked as an ICT/IP lawyer and policy consultant in the Netherlands. Ben holds a degree in law, specialising in Information Law.

Pamina Smith currently serves as an Assistant Editor at the Oxford Internet Institute and recently completed an MPhil in Comparative Social Policy at the University of Oxford. She previously worked as Assistant Policy Officer at the European Commission, handling broadband policy and telecommunications regulation, and has a degree in the History and Literature of Modern Europe from Harvard College.

Though there is nothing qualitatively new about involving more people in government and policy processes, digital technologies in principle make it possible to increase the quantity of such involvement dramatically, by lowering the costs of participation (Margetts et al., 2015) and making it possible to tap into people’s free time (Shirky, 2010). This difference in quantity is arguably great enough to obtain a quality of its own. We can thus be justified in using the term “crowdsourcing for public policy and government” to refer to new digitally enabled ways of involving people in any aspect of democratic politics and government, not replacing but rather augmenting more traditional participation routes such as elections and referendums.

In this editorial, we will briefly highlight some of the key emerging issues in research on crowdsourcing for public policy and government. Our entry point into the discussion is a collection of research papers first presented at the Internet, Politics & Policy 2014 (IPP2014) conference organized by the Oxford Internet Institute (University of Oxford) and the Policy & Internet journal. The theme of this very successful conference—our third since the founding of the journal—was “crowdsourcing for politics and policy.” Out of almost 80 papers presented at the conference in September last year, 14 of the best have now been published as peer-reviewed articles in this journal, including five in this issue. A further handful of papers from the conference focusing on labor issues will be published in the next issue, but we can already now take stock of all the articles focusing on government, politics, and policy.

The growing interest in crowdsourcing for government and public policy must be understood in the context of the contemporary malaise of politics, which is being felt across the democratic world, but most of all in Europe. The problems with democracy have a long history, from the declining powers of parliamentary bodies when compared to the executive; to declining turnouts in elections, declining participation in mass parties, and declining trust in democratic institutions and politicians. But these problems have gained a new salience in the last five years, as the ongoing financial crisis has contributed to the rise of a range of new populist forces all across Europe, and to a fragmentation of the center ground. Furthermore, poor accuracy of pre- election polls in recent elections in Israel and the UK have generated considerable debate over the usefulness and accuracy of the traditional way of knowing what the public is thinking: the sample survey.

Many place hopes on technological and institutional innovations such as crowdsourcing to show a way out of the brewing crisis of democratic politics and political science. One of the key attractions of crowdsourcing techniques to governments and grass roots movements alike is the legitimacy such techniques are expected to be able to generate. For example, crowdsourcing techniques have been applied to enable citizens to verify the legality and correctness of government decisions and outcomes. A well-known application is to ask citizens to audit large volumes of data on government spending, to uncover any malfeasance but also to increase citizens’ trust in the government (Maguire, 2011).

Articles emerging from the IPP2014 conference analyze other interesting and comparable applications. In an article titled “Population as Auditor of an Election Process in Honduras: The Case of the VotoSocial Crowdsourcing Platform,” Carlos Arias, Jorge Garcia and Alejandro Corpeño (2015) describe the use of crowdsourcing for auditing election results. Dieter Zinnbauer (2015) discusses the potentials and pitfalls of the use of crowdsourcing for some other types of auditing purposes, in “Crowdsourced Corruption Reporting: What Petrified Forests, Street Music, Bath Towels, and the Taxman Can Tell Us About the Prospects for Its Future.”

Besides allowing citizens to verify the outcome of a process, crowdsourcing can also be used to lend an air of inclusiveness and transparency to a process itself. This process legitimacy can then indirectly legitimate the outcome of the process as well. For example, crowdsourcing-style open processes have been used to collect policy ideas, gather support for difficult policy decisions, and even generate detailed spending plans through participatory budgeting (Wampler & Avritzer, 2004). Articles emerging from our conference further advance this line of research. Roxana Radu, Nicolo Zingales and Enrico Calandro (2015) examine the use of crowdsourcing to lend process legitimacy to Internet governance, in an article titled “Crowdsourcing Ideas as an Emerging Form of Multistakeholder Participation in Internet Governance.” Graham Smith, Robert C. Richards Jr. and John Gastil (2015) write about “The Potential of Participedia as a Crowdsourcing Tool for Comparative Analysis of Democratic Innovations.”

An interesting cautionary tale is presented by Henrik Serup Christensen, Maija Karjalainen and Laura Nurminen (2015) in “Does Crowdsourcing Legislation Increase Political Legitimacy? The Case of Avoin Ministeriö in Finland.” They show how a citizen initiative process ended up decreasing government legitimacy, after the government failed to implement the outcome of an initiative process that was perceived as highly legitimate by its supporters. Taneli Heikka (2015) further examines the implications of citizen initiative processes to the state–citizen relationship in “The Rise of the Mediating Citizen: Time, Space and Citizenship in the Crowdsourcing of Finnish Legislation.”

In many of the contributions that touch on the legitimating effects of crowdsourcing, one can sense a third, latent theme. Besides allowing outcomes to be audited and processes to be potentially more inclusive, crowdsourcing can also increase the perceived legitimacy of a government or policy process by lending an air of innovation and technological progress to the endeavor and those involved in it. This is most explicitly stated by Simo Hosio, Jorge Goncalves, Vassilis Kostakos and Jukka Riekki (2015) in “Crowdsourcing Public Opinion Using Urban Pervasive Technologies: Lessons From Real-Life Experiments in Oulu.” They describe how local government officials collaborating with the research team to test a new public screen based polling system “expressed that the PR value boosted their public perception as a modern organization.” That some government crowdsourcing initatives are at least in part motivated by such “crowdwashing” is hardly surprising, but it encourages us to retain a critical approach and analyze actual outcomes instead of accepting dominant discourses about the nature and effects of crowdsourcing at face value.

For instance, we must continue to examine the actual size, composition, internal structures and motivations of the supposed “crowds” that make use of online platforms. Articles emerging from our conference that contributed towards this aim include “Event Prediction With Learning Algorithms—A Study of Events Surrounding the Egyptian Revolution of 2011 on the Basis of Micro Blog Data” by Benedikt Boecking, Margeret Hall and Jeff Schneider (2015) and “Cyber Hate Speech on Twitter: An Application of Machine Classification and Statistical Modeling for Policy and Decision Making” by Pete Burnap and Matthew L. Williams (2015). Anatoliy Gruzd and Ksenia Tsyganova won a best paper award at the IPP2014 conference for an article published in this journal as “Information Wars and Online Activism During the 2013/2014 Crisis in Ukraine: Examining the Social Structures of Pro- and Anti-Maidan Groups.” These articles can be used to challenge the notion that crowdsourcing contributors are simply sets of independent individuals who are neatly representative of a larger population, and instead highlight the clusters, networks, and power structures inherent within them. This has implications to the democratic legitimacy of some of the more naive crowdsourcing initiatives.

One of the most original articles to emerge out of IPP2014 turns the concept of crowdsourcing for public policy and government on its head. While most research has focused on crowdsourcing’s empowering effects (or lack thereof), Gregory Asmolov (2015) analyses crowdsourcing as a form of social control. In an article titled “Vertical Crowdsourcing in Russia: Balancing Governance of Crowds and State–Citizen Partnership in Emergency Situations,” Asmolov draws on empirical evidence and theorists such as Foucault to show how crowdsourcing platforms can be used to institutionalize volunteer resources in order to align them with state objectives and prevent independent collective action. An article by Jorge Goncalves, Yong Liu, Bin Xiao, Saad Chaudhry, Simo Hosio and Vassilis Kostakos (2015) provides a less nefarious example of strategic use of online platforms to further government objectives, under the title “Increasing the Reach of Government Social Media: A Case Study in Modeling Government–Citizen Interaction on Facebook.”

Articles emerging from the conference also include two review articles that provide useful overviews of the field from different perspectives. “A Systematic Review of Online Deliberation Research” by Dennis Friess and Christiane Eilders (2015) takes stock of the use of digital technologies as public spheres. “The Fundamentals of Policy Crowdsourcing” by John Prpić, Araz Taeihagh and James Melton (2015) situates a broad variety of crowdsourcing literature into the context of a public policy cycle framework.

It has been extremely satisfying to follow the progress of these papers from initial conference submissions to high-quality journal articles, and to see that the final product not only advances the state of the art, but also provides certain new and critical perspectives on crowdsourcing. These perspectives will no doubt provoke responses, and Policy & Internet continues to welcome high-quality submissions dealing with crowdsourcing for public policy, government, and beyond.

Read the full editorial: Vili Lehdonvirta andJonathan Bright (2015) Crowdsourcing for Public Policy and Government. Editorial. Volume 7, Issue 3, pages 263–267.

References

Arias, C.R., Garcia, J. and Corpeño, A. (2015) Population as Auditor of an Election Process in Honduras: The Case of the VotoSocial Crowdsourcing Platform. Policy & Internet 7 (2) 185–202.

Asmolov, G. (2105) Vertical Crowdsourcing in Russia: Balancing Governance of Crowds and State–Citizen Partnership in Emergency Situations. Policy & Internet 7 (3).

Brabham, D. C. (2013). Citizen E-Participation in Urban Governance: Crowdsourcing and Collaborative Creativity: Crowdsourcing and Collaborative Creativity. IGI Global.

Boecking, B., Hall, M. and Schneider, J. (2015) Event Prediction With Learning Algorithms—A Study of Events Surrounding the Egyptian Revolution of 2011 on the Basis of Micro Blog Data. Policy & Internet 7 (2) 159–184.

Burnap P. and Williams, M.L. (2015) Cyber Hate Speech on Twitter: An Application of Machine Classification and Statistical Modeling for Policy and Decision Making. Policy & Internet 7 (2) 223–242.

Christensen, H.S., Karjalainen, M. and Nurminen, L. (2015) Does Crowdsourcing Legislation Increase Political Legitimacy? The Case of Avoin Ministeriö in Finland. Policy & Internet 7 (1) 25-45.

Friess, D. and Eilders, C. (2015) A Systematic Review of Online Deliberation Research. Policy & Internet 7 (3).

Goncalves, J., Liu, Y., Xiao, B., Chaudhry, S., Hosio, S. and Kostakos, V. (2015) Increasing the Reach of Government Social Media: A Case Study in Modeling Government–Citizen Interaction on Facebook. Policy & Internet 7 (1) 80-102.

Gruzd, A. and Tsyganova, K. (2015) Information Wars and Online Activism During the 2013/2014 Crisis in Ukraine: Examining the Social Structures of Pro- and Anti-Maidan Groups. Policy & Internet 7 (2) 121–158.

Heikka, T. (2015) The Rise of the Mediating Citizen: Time, Space and Citizenship in the Crowdsourcing of Finnish Legislation. Policy & Internet 7 (3).

Hosio, S., Goncalves, J., Kostakos, V. and Riekki, J. (2015) Crowdsourcing Public Opinion Using Urban Pervasive Technologies: Lessons From Real-Life Experiments in Oulu. Policy & Internet 7 (2) 203–222.

Howe, J. (2006). The Rise of Crowdsourcing by Jeff Howe | Byliner. Retrieved from

Maguire, S. (2011). Can Data Deliver Better Government? Political Quarterly, 82(4), 522–525.

Margetts, H., John, P., Hale, S., & Yasseri, T. (2015): Political Turbulence: How Social Media Shape Collective Action. Princeton University Press.

Prpić, J., Taeihagh, A. and Melton, J. (2015) The Fundamentals of Policy Crowdsourcing. Policy & Internet 7 (3).

Radu, R., Zingales, N. and Calandro, E. (2015) Crowdsourcing Ideas as an Emerging Form of Multistakeholder Participation in Internet Governance. Policy & Internet 7 (3).

Shirky, C. (2010). Cognitive Surplus: How Technology Makes Consumers into Collaborators. Penguin Publishing Group.

Smith, G., Richards R.C. Jr. and Gastil, J. (2015) The Potential of Participedia as a Crowdsourcing Tool for Comparative Analysis of Democratic Innovations. Policy & Internet 7 (2) 243–262.

Wampler, B., & Avritzer, L. (2004). Participatory publics: civil society and new institutions in democratic Brazil. Comparative Politics, 36(3), 291–312.

Zinnbauer, D. (2015) Crowdsourced Corruption Reporting: What Petrified Forests, Street Music, Bath Towels, and the Taxman Can Tell Us About the Prospects for Its Future. Policy & Internet 7 (1) 1–24.

Ride-hailing app Uber is close to replacing government-licensed taxis in some cities, while Airbnb’s accommodation rental platform has become a serious competitor to government-regulated hotel markets. Many other apps and platforms are trying to do the same in other sectors of the economy. In my previous post, I argued that platforms can be viewed in social science terms as economic institutions that provide infrastructures necessary for markets to thrive. I explained how the natural selection theory of institutional change suggests that people are migrating from state institutions to these new code-based institutions because they provide a more efficient environment for doing business. In this article, I will discuss some of the problems with this theory, and outline a more nuanced theory of institutional change that suggests that platforms’ effects on society will be complex and influence different people in different ways.

Economic sociologists like Neil Fligstein have pointed out that not everyone is as free to choose the means through which they conduct their trade. For example, if buyers in a market switch to new institutions, sellers may have little choice but to follow, even if the new institutions leave them worse off than the old ones did. Even if taxi drivers don’t like Uber’s rules, they may find that there is little business to be had outside the platform, and switch anyway. In the end, the choice of institutions can boil down to power. Economists have shown that even a small group of participants with enough market power — like corporate buyers — may be able to force a whole market to tip in favour of particular institutions. Uber offers a special solution for corporate clients, though I don’t know if this has played any part in the platform’s success.

Even when everyone participates in an institutional arrangement willingly, we still can’t assume that it will contribute to the social good. Cambridge economic historian Sheilagh Ogilvie has pointed out that an institution that is efficient for everyone who participates in it can still be inefficient for society as a whole if it affects third parties. For example, when Airbnb is used to turn an ordinary flat into a hotel room, it can cause nuisance to neighbours in the form of noise, traffic, and guests unfamiliar with the local rules. The convenience and low cost of doing business through the platform is achieved in part at others’ expense. In the worst case, a platform can make society not more but less efficient — by creating a ‘free rider economy’.

In general, social scientists recognize that different people and groups in society often have conflicting interests in how economic institutions are shaped. These interests are reconciled — if they are reconciled — through political institutions. Many social scientists thus look not so much at efficiencies but at political institutions to understand why economic institutions are shaped the way they are. For example, a democratic local government in principle represents the interests of its citizens, through political institutions such as council elections and public consultations. Local governments consequently try to strike a balance between the conflicting interests of hoteliers and their neighbours, by limiting hotel business to certain zones. In contrast, Airbnb as a for-profit business must cater to the interests of its customers, the would-be hoteliers and their guests. It has no mechanism, and more importantly, no mandate, to address on an equal footing the interests of third parties like customers’ neighbours. Perhaps because of this, 74% of Airbnb’s properties are not in the main hotel districts, but in ordinary residential blocks.

That said, governments have their own challenges in producing fair and efficient economic institutions. Not least among these is the fact that government regulators are at a risk of capture by incumbent market participants, or at the very least they face the innovator’s dilemma: it is easier to craft rules that benefit the incumbents than rules that provide great but uncertain benefits to future market participants. For example, cities around the world operate taxi licensing systems, where only strictly limited numbers of license owners are allowed to operate taxicabs. Whatever benefits this system offers to customers in terms of quality assurance, among its biggest beneficiaries are the license owners, and among its losers the would-be drivers who are excluded from the market. Institutional insiders and outsiders have conflicting interests, and government political institutions are often such that it is easier for it to side with the insiders.

Against this background, platforms appear almost as radical reformers that provide market access to those whom the establishment has denied it. For example, Uber recently announced that it aims to create one million jobs for women by 2020, a bold pledge in the male-dominated transport industry, and one that would likely not be possible if it adhered to government licensing requirements, as most licenses are owned by men. Having said that, Uber’s definition of a ‘job’ is something much more precarious and entrepreneurial than the conventional definition. My point here is not to side with either Uber or the licensing system, but to show that their social implications are very different. Both possess at least some flaws as well as redeeming qualities, many of which can be traced back to their political institutions and whom they represent.

What kind of new economic institutions are platform developers creating? How efficient are they? What other consequences, including unintended ones, do they have and to whom? Whose interests are they geared to represent — capital vs. labour, consumer vs. producer, Silicon Valley vs. local business, incumbent vs. marginalized? These are the questions that policy makers, journalists, and social scientists ought to be asking at this moment of transformation in our economic institutions. Instead of being forced to choose one or the other between established institutions and platforms as they currently are, I hope that we will be able to discover ways to take what is good in both, and create infrastructure for an economy that is as fair and inclusive as it is efficient and innovative.

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.

Cars were smashed and tires burned in France last month in protests against the ride hailing app Uber. Less violent protests have also been staged against Airbnb, a platform for renting short-term accommodation. Despite the protests, neither platform shows any signs of faltering. Uber says it has a million users in France, and is available in 57 countries. Airbnb is available in over 190 countries, and boasts over a million rooms, more than hotel giants like Hilton and Marriott. Policy makers at the highest levels are starting to notice the rise of these and similar platforms. An EU Commission flagship strategy paper notes that “online platforms are playing an ever more central role in social and economic life,” while the Federal Trade Commission recently held a workshop on the topic in Washington.

Journalists and entrepreneurs have been quick to coin terms that try to capture the essence of the social and economic changes associated with online platforms: the sharing economy; the on-demand economy; the peer-to-peer economy; and so on. Each perhaps captures one aspect of the phenomenon, but doesn’t go very far in helping us make sense of all its potentials and contradictions, including why some people love it and some would like to smash it into pieces. Instead of starting from the assumption that everything we see today is new and unprecedented, what if we dug into existing social science theory to see what it has to say about economic transformation and the emergence of markets?

Economic sociologists are adamant that markets don’t just emerge by themselves: they are always based on some kind of an underlying infrastructure that allows people to find out what goods and services are on offer, agree on prices and terms, pay, and have a reasonable expectation that the other party will honour the agreement. The oldest market infrastructure is the personal social network: traders hear what’s on offer through word of mouth and trade only with those whom they personally know and trust. But personal networks alone couldn’t sustain the immense scale of trading in today’s society. Every day we do business with strangers and trust them to provide for our most basic needs. This is possible because modern society has developed institutions — things like private property, enforceable contracts, standardized weights and measures, consumer protection, and many other general and sector specific norms and facilities. By enabling and constraining everyone’s behaviours in predictable ways, institutions constitute a robust and more inclusive infrastructure for markets than personal social networks.

Modern institutions didn’t of course appear out of nowhere. Between prehistoric social networks and the contemporary institutions of the modern state, there is a long historical continuum of economic institutions, from ancient trade routes with their customs to medieval fairs with their codes of conduct to state-enforced trade laws of the early industrial era. Institutional economists led by Oliver Williamson and economic historians led by Douglass North theorized in the 1980s that economic institutions evolve towards more efficient forms through a process of natural selection. As new institutional forms become possible thanks to technological and organizational innovation, people switch to cheaper, easier, more secure, and overall more efficient institutions out of self-interest. Old and cumbersome institutions fall into disuse, and society becomes more efficient and economically prosperous as a result. Williamson and North both later received the Nobel Memorial Prize in Economic Sciences.

It is easy to frame platforms as the next step in such an evolutionary process. Even if platforms don’t replace state institutions, they can plug gaps that remain the state-provided infrastructure. For example, enforcing a contract in court is often too expensive and unwieldy to be used to secure transactions between individual consumers. Platforms provide cheaper and easier alternatives to formal contract enforcement, in the form of reputation systems that allow participants to rate each others’ conduct and view past ratings. Thanks to this, small transactions like sharing a commute that previously only happened in personal networks can now potentially take place on a wider scale, resulting in greater resource efficiency and prosperity (the ‘sharing economy’). Platforms are not the first companies to plug holes in state-provided market infrastructure, though. Private arbitrators, recruitment agencies, and credit rating firms have been doing similar things for a long time.

What’s arguably new about platforms, though, is that some of the most popular ones are not mere complements, but almost complete substitutes to state-provided market infrastructures. Uber provides a complete substitute to government-licensed taxi infrastructures, addressing everything from quality and discovery to trust and payment. Airbnb provides a similarly sweeping solution to short-term accommodation rental. Both platforms have been hugely successful; in San Francisco, Uber has far surpassed the city’s official taxi market in size. The sellers on these platforms are not just consumers wanting to make better use of their resources, but also firms and professionals switching over from the state infrastructure. It is as if people and companies were abandoning their national institutions and emigrating en masse to Platform Nation.

From the natural selection perspective, this move from state institutions to platforms seems easy to understand. State institutions are designed by committee and carry all kinds of historical baggage, while platforms are designed from the ground up to address their users’ needs. Government institutions are geographically fragmented, while platforms offer a seamless experience from one city, country, and language area to the other. Government offices have opening hours and queues, while platforms make use of latest technologies to provide services around the clock (the ‘on-demand economy’). Given the choice, people switch to the most efficient institutions, and society becomes more efficient as a result. The policy implications of the theory are that government shouldn’t try to stop people from using Uber and Airbnb, and that it shouldn’t try to impose its evidently less efficient norms on the platforms. Let competing platforms innovate new regulatory regimes, and let people vote with their feet; let there be a market for markets.

The natural selection theory of institutional change provides a compellingly simple way to explain the rise of platforms. However, it has difficulty in explaining some important facts, like why economic institutions have historically developed differently in different places around the world, and why some people now protest vehemently against supposedly better institutions. Indeed, over the years since the theory was first introduced, social scientists have discovered significant problems in it. Economic sociologists like Neil Fligstein have noted that not everyone is as free to choose the institutions that they use. Economic historian Sheilagh Ogilvie has pointed out that even institutions that are efficient for those who participate in them can still sometimes be inefficient for society as a whole. These points suggest a different theory of institutional change, which I will apply to online platforms in my next post.

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.

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.

This may well make Twitter particularly well suited to political debate and activity. Yet important questions emerge in terms of the patterns of conduct and engagement. Chief among them: are users mainly seeking to reinforce their own viewpoints and link with likeminded persons, or is there a basis for widening and thoughtful exposure to a variety of perspectives that may improve the collective intelligence of the citizenry as a result?

Conflict and Polarization

Political polarization often occurs in a so-called ‘echo chamber’ environment, in which individuals are exposed to only information and communities that support their own viewpoints, while ignoring opposing perspectives and insights. In such isolating and self-reinforcing conditions, ideas can become more engrained and extreme due to lack of contact with contradictory views and the exchanges that could ensue as a result.

On the web, political polarization has been found among political blogs, for instance. American researchers have found that liberal and conservative bloggers in the US tend to link to other bloggers who share their political ideology. For Kingwell, a prominent Canadian philosopher, the resulting dynamic is one that can be characterized by a decline in civility and a lessening ability for political compromise to take hold. He laments the emergence of a ‘shout doctrine’ that corrodes the civic and political culture, in the sense that divisions are accentuated and compromise becomes more elusive.

Such a dynamic is not the result of social media alone – but rather it reflects for some the impacts of the Internet generally and the specific manner by which social media can lend itself to broadcasting and sensationalism, rather than reasoned debate and exchange. Traditional media and journalistic organizations have thus become further pressured to act in kind, driven less by a patient and persistent presentation of all sides of an issue and more by near-instantaneous reporting online. In a manner akin to Kingwell’s view, one prominent television news journalist in the US, Ted Koppel, has lamented this new media environment as a danger to the republic.

Nonetheless, the research is far from conclusive as to whether the Internet increases political polarization. Some studies have found that among casual acquaintances (such as those that can typically be observed on Twitter), it is common to observe connections across ideological boundaries. In one such funded by the Pew Internet and American Life Project and the National Science Foundation, findings suggest that people who often visit websites that support their ideological orientation also visit web sites that support divergent political views. As a result, greater sensitivity and empathy for alternative viewpoints could potentially ensue, improving the likelihood for political compromise – even on a modest scale that would otherwise not have been achievable without this heightened awareness and debate.

Early Evidence from Canada

The 2011 federal election in Canada was dubbed by some observers in the media as the country’s first ‘social media election’ – as platforms such as Facebook and Twitter became prominent sources of information for growing segments of the citizenry, and evermore strategic tools for political parties in terms of fundraising, messaging, and mobilizing voters. In examining Twitter traffic, our own intention was to ascertain the extent to which polarization or cross-pollinization was occurring across the portion of the electorate making use of this micro-blogging platform.

We gathered nearly 6000 tweets pertaining to the federal election made by just under 1500 people during a three-day period in the week preceding election day (this time period was chosen because it was late enough in the campaign for people to have an informed opinion, but still early enough for them to be persuaded as to how they should vote). Once the tweets were retrieved, we used social network analysis and content analysis to analyze patterns of exchange and messaging content in depth.

We found that overall people do tend to cluster around shared political views on Twitter. Supporters of each of the four major political parties identified in the study were more likely to tweet to other supporters of the same affiliation (this was particularly true of the ruling Conservatives, the most inwardly networked of the four major politically parties). Nevertheless, in a significant number of cases (36% of all interactions) we also observed a cross-ideological discourse, especially among supporters of the two most prominent left-of-centre parties, the New Democratic Party (NDP) and the Liberal Party of Canada (LPC). The cross-ideological interactions among supporters of left-leaning parties tended to be agreeable in nature, but often at the expense of the party in power, the Conservative Party of Canada (CPC). Members from the NDP and Liberal formations were also more likely to share general information and updates about the election as well as debate various issues around their party platforms with each other.

By contrast, interactions between parties that are ideologically distant seemed to denote a tone of conflict: nearly 40% of tweets between left-leaning parties and the Conservatives tended to be hostile. Such negative interactions between supporters of different parties have shown to reduce enthusiasm about political campaigns in general, potentially widening the cleavage between highly engaged partisans and less affiliated citizens who may view such forms of aggressive and divisive politics as distasteful.

For Twitter sceptics, one concern is that the short length of Twitter messages does not allow for meaningful and in-depth discussions around complex political issues. While it is certainly true that expression within 140 characters is limited, one third of tweets between supporters of different parties included links to external sources such as news stories, blog posts, or YouTube videos. Such indirect sourcing can thereby constitute a means of expanding dialogue and debate.

Accordingly, although it is common to view Twitter as largely a platform for self-expression via short tweets, there may be a wider collective dimension to both users and the population at large as a steady stream of both individual viewpoints and referenced sources drive learning and additional exchange. If these exchanges happen across partisan boundaries, they can contribute to greater collective awareness and learning for the citizenry at large.

As the next federal election approaches in 2015, with younger voters gravitating online – especially via mobile devices, and with traditional polling increasingly under siege as less reliable than in the past, all major parties will undoubtedly devote more energy and resources to social media strategies including, perhaps most prominently, an effective usage of Twitter.

Partisan Politics versus Politics 2.0

In a still-nascent era likely to be shaped by the rise of social media and a more participative Internet on the one hand, and the explosion of ‘big data’ on the other hand, the prominence of Twitter in shaping political discourse seems destined to heighten. Our preliminary analysis suggests an important cleavage between traditional political processes and parties – and wider dynamics of political learning and exchange across a changing society that is more fluid in its political values and affiliations.

Within existing democratic structures, Twitter is viewed by political parties as primarily a platform for messaging and branding, thereby mobilizing members with shared viewpoints and attacking opposing interests. Our own analysis of Canadian electoral tweets both amongst partisans and across party lines underscores this point. The nexus between partisan operatives and new media formations will prove to be an increasingly strategic dimension to campaigning going forward.

More broadly, however, Twitter is a source of information, expression, and mobilization across a myriad of actors and formations that may not align well with traditional partisan organizations and identities. Social movements arising during the Arab Spring, amongst Mexican youth during that country’s most recent federal elections and most recently in Ukraine are cases in point. Across these wider societal dimensions – especially consequential in newly emerging democracies, the tremendous potential of platforms such as Twitter may well lie in facilitating new and much more open forms of democratic engagement that challenge our traditional constructs.

In sum, we are witnessing the inception of new forms of what can be dubbed ‘Politics 2.0’ that denotes a movement of both opportunities and challenges likely to play out differently across democracies at various stages of socio-economic, political, and digital development. Whether Twitter and other likeminded social media platforms enable inclusive and expansionary learning, or instead engrain divisive polarized exchange, has yet to be determined. What is clear however is that on Twitter, in some instances, birds of a feather do flock together as they do on political blogs. But in other instances, Twitter can play an important role to foster cross parties communication in the online political arenas.

Read the full article: Gruzd, A., and Roy, J. (2014) Investigating Political Polarization on Twitter: A Canadian Perspective. Policy and Internet 6 (1) 28-48.

Also read: Gruzd, A. and Tsyganova, K. Information wars and online activism during the 2013/2014 crisis in Ukraine: Examining the social structures of Pro- and Anti-Maidan groups. Policy and Internet. Early View April 2015: DOI: 10.1002/poi3.91

Anatoliy Gruzd is Associate Professor in the Ted Rogers School of Management and Director of the Social Media Lab at Ryerson University, Canada. Jeffrey Roy is Professor in the School of Public Administration at Dalhousie University’s Faculty of Management. His most recent book was published in 2013 by Springer: From Machinery to Mobility: Government and Democracy in a Participative Age.