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Bursting the bubbles of the Arab Spring: the brokers who bridge ideology on Twitter

Online activism has become increasingly visible, with social media platforms being used to express protest and dissent from the Arab Spring to #MeToo. Scholarly interest in online activism has grown with its use, together with disagreement about its impact. Do social media really challenge traditional politics? Some claim that social media have had a profound and positive effect on modern protest — the speed of information sharing making online networks highly effective in building revolutionary movements. Others argue that this activity is merely symbolic: online activism has little or no impact, dilutes offline activism, and weakens social movements. Given online activity doesn’t involve the degree of risk, trust, or effort required on the ground, they argue that it can’t be considered to be “real” activism. In this view, the Arab Spring wasn’t simply a series of “Twitter revolutions”.

Despite much work on offline social movements and coalition building, few studies have used social network analysis to examine the influence of brokers of online activists (i.e. those who act as a bridge between different ideological groups), or their role in information diffusion across a network. In her Policy & Internet article “Brokerage Roles and Strategic Positions in Twitter Networks of the 2011 Egyptian Revolution”, Deena Abul-Fottouh tests whether social movements theory of networks and coalition building — developed to explain brokerage roles in offline networks, between established parties and organisations — can also be used to explain what happens online.

Social movements theory suggests that actors who occupy an intermediary structural position between different ideological groups are more influential than those embedded only in their own faction. That is, the “bridging ties” that link across political ideologies have a greater impact on mobilization than the bonding ties within a faction. Indeed, examining the Egyptian revolution and ensuing crisis, Deena finds that these online brokers were more evident during the first phase of movement solidarity between liberals, islamists, and socialists than in the period of schism and crisis (2011-2014) that followed the initial protests. However, she also found that the online brokers didn’t match the brokers on the ground: they played different roles, complementing rather than mirroring each other in advancing the revolutionary movement.

We caught up with Deena to discuss her findings:

Ed: Firstly: is the “Arab Spring” a useful term? Does it help to think of the events that took place across parts of the Middle East and North Africa under this umbrella term — which I suppose implies some common cause or mechanism?

Deena: Well, I believe it’s useful to an extent. It helps describe some positive common features that existed in the region such as dissatisfaction with the existing regimes, a dissatisfaction that was transformed from the domain of advocacy to the domain of high-risk activism, a common feeling among the people that they can make a difference, even though it did not last long, and the evidence that there are young people in the region who are willing to sacrifice for their freedom. On the other hand, structural forces in the region such as the power of deep states and the forces of counter-revolution were capable of halting this Arab Spring before it burgeoned or bore fruit, so may be the term “Spring” is no longer relevant.

Ed: Revolutions have been happening for centuries, i.e. they obviously don’t need Twitter or Facebook to happen. How significant do you think social media were in this case, either in sparking or sustaining the protests? And how useful are these new social media data as a means to examine the mechanisms of protest?

Deena: Social media platforms have proven to be useful in facilitating protests such as by sharing information in a speedy manner and on a broad range across borders. People in Egypt and other places in the region were influenced by Tunisia, and protest tactics were shared online. In other words, social media platforms definitely facilitate diffusion of protests. They are also hubs to create a common identity and culture among activists, which is crucial for the success of social movements. I also believe that social media present activists with various ways to circumvent policing of activism (e.g. using pseudonyms to hide the identity of the activists, sharing information about places to avoid in times of protests, many platforms offer the possibility for activists to form closed groups where they have high privacy to discuss non-public matters, etc.).

However, social media ties are weak ties. These platforms are not necessarily efficient in building the trust needed to bond social movements, especially in times of schism and at the level of high-risk activism. That is why, as I discuss in my article, we can see that the type of brokerage that is formed online is brokerage that is built on weak ties, not necessarily the same as offline brokerage that usually requires high trust.

Ed: It’s interesting that you could detect bridging between groups. Given schism seems to be fairly standard in society (Cf filter bubbles etc.) .. has enough attention been paid to this process of temporary shifting alignments, to advance a common cause? And are these incidental, or intentional acts of brokerage?

Deena: I believe further studies need to be made on the concepts of solidarity, schism and brokerage within social movements both online and offline. Little attention has been given to how movements come together or break apart online. The Egyptian revolution is a rich case to study these concepts as the many changes that happened in the path of the revolution in its first five years and the intervention of different forces have led to multiple shifts of alliances that deserve study. Acts of brokerage do not necessarily have to be intentional. In social movements studies, researchers have studied incidental acts that could eventually lead to formation of alliances, such as considering co-members of various social movements organizations as brokers between these organizations.

I believe that the same happens online. Brokerage could start with incidental acts such as activists following each other on Twitter for example, which could develop into stronger ties through mentioning each other. This could also build up to coordinating activities online and offline. In the case of the Egyptian revolution, many activists who met in protests on the ground were also friends online. The same happened in Moldova where activists coordinated tactics online and met on the ground. Thus, incidental acts that start with following each other online could develop into intentional coordinated activism offline. I believe further qualitative interviews need to be conducted with activists to study how they coordinate between online and offline activism, as there are certain mechanisms that cannot be observed through just studying the public profiles of activists or their structural networks.

Ed: The “Arab Spring” has had a mixed outcome across the region — and is also now perhaps a bit forgotten in the West. There have been various network studies of the 2011 protests: but what about the time between visible protests .. isn’t that in a way more important? What would a social network study of the current situation in Egypt look like, do you think?

Deena: Yes, the in-between times of waves of protests are as important to study as the waves themselves as they reveal a lot about what could happen, and we usually study them retroactively after the big shocks happen. A social network of the current situation in Egypt would probably include many “isolates” and tiny “components”, if I would use social network analysis terms. This started showing in 2014 as the effects of schism in the movement. I believe this became aggravated over time as the military coup d’état got a stronger grip over the country, suppressing all opposition. Many activists are either detained or have left the country. A quick look at their online profiles does not reveal strong communication between them. Yet, this is what apparently shows from public profiles. One of the levers that social media platforms offer is the ability to create private or “closed” groups online.

I believe these groups might include rich data about activists’ communication. However, it is very difficult, almost impossible to study these groups, unless you are a member or they give you permission. In other words, there might be some sort of communication occurring between activists but at a level that researchers unfortunately cannot access. I think we might call it the “underground of online activism”, which I believe is potentially a very rich area of study.

Ed: A standard criticism of “Twitter network studies” is that they aren’t very rich — they may show who’s following whom, but not necessarily why, or with what effect. Have there been any larger, more detailed studies of the Arab Spring that take in all sides: networks, politics, ethnography, history — both online and offline?

Deena: To my knowledge, there haven’t been studies that have included all these aspects together. Yet there are many studies that covered each of them separately, especially the politics, ethnography, and history of the Arab Spring (see for example: Egypt’s Tahrir Revolution 2013, edited by D. Tschirgi, W. Kazziha and S. F. McMahon). Similarly, very few studies have tried to compare the online and offline repertoires (see for example: Weber, Garimella and Batayneh 2013, Abul-Fottouh and Fetner 2018). In my doctoral dissertation (2018 from McMaster University), I tried to include many of these elements.

Read the full article: Abul-Fottouh, D. (2018) Brokerage Roles and Strategic Positions in Twitter Networks of the 2011 Egyptian Revolution. Policy & Internet 10: 218-240. doi:10.1002/poi3.169

Deena Abul-Fottouh was talking to blog editor David Sutcliffe.

In a world of “connective action” — what makes an influential Twitter user?

A significant part of political deliberation now takes place on online forums and social networking sites, leading to the idea that collective action might be evolving into “connective action”. The new level of connectivity (particularly of social media) raises important questions about its role in the political process. but understanding important phenomena, such as social influence, social forces, and digital divides, requires analysis of very large social systems, which traditionally has been a challenging task in the social sciences.

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

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

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

We caught up with the authors to discuss their findings:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

David Garcia was talking to blog editor David Sutcliffe.

Does Twitter now set the news agenda?

The information provided in the traditional media is of fundamental importance for the policy-making process, signalling which issues are gaining traction, which are falling out of favour, and introducing entirely new problems for the public to digest. But the monopoly of the traditional media as a vehicle for disseminating information about the policy agenda is being superseded by social media, with Twitter in particular used by politicians to influence traditional news content.

In their Policy & Internet article, “Politicians and the Policy Agenda: Does Use of Twitter by the U.S. Congress Direct New York Times Content?” Matthew A. Shapiro and Libby Hemphill examine the extent to which he traditional media is influenced by politicians’ Twitter posts. They draw on indexing theory, which states that media coverage and framing of key policy issues will tend to track elite debate. To understand why the newspaper covers an issue and predict the daily New York Times content, it is modelled as a function of all of the previous day’s policy issue areas as well as all of the previous day’s Twitter posts about all of the policy issue areas by Democrats and Republicans.

They ask to what extent are the agenda-setting efforts of members of Congress acknowledged by the traditional media; what, if any, the advantages are for one party over the other, measured by the traditional media’s increased attention; and whether there is any variance across different policy issue areas? They find that Twitter is a legitimate political communication vehicle for US officials, that journalists consider Twitter when crafting their coverage, and that Twitter-based announcements by members of Congress are a valid substitute for the traditional communiqué in journalism, particularly for issues related to immigration and marginalized groups, and issues related to the economy and health care.

We caught up with the authors to discuss their findings:

Ed.: Can you give a quick outline of media indexing theory? Does it basically say that the press reports whatever the elite are talking about? (i.e. that press coverage can be thought of as a simple index, which tracks the many conversations that make up elite debate).

Matthew: Indexing theory, in brief, states that the content of media reports reflects the degree to which elites – politicians and leaders in government in particular – are in agreement or disagreement. The greater the level of agreement or consensus among elites, the less news there is to report in terms of elite conflict. This is not to say that a consensus among elites is not newsworthy; indexing theory conveys how media reporting is a function of the multiple voices that exist when there is elite debate.

Ed.: You say Twitter seemed a valid measure of news indexing (i.e. coverage) for at least some topics. Could it be that the NYT isn’t following Twitter so much as Twitter (and the NYT) are both following something else, i.e. floor debates, releases, etc.?

Matthew: We can’t test for whether the NYT is following Twitter rather than floor debates/press releases without collecting data for the latter. Perhaps If the House and Senate Press Galleries are indexing the news based on House and Senate debates, and if Twitter posts by members of Congress reflect the House and Senate discussions, we could still argue that Twitter remains significant because there are no limits on the amount of discussion – i.e. the boundaries of the House and Senate floors no longer exist – and the media are increasingly reliant on politicians’ use of Twitter to communicate to the press. In any case, the existing research shows that journalists are increasingly relying on Twitter posts for updates from elites.

Ed.: I’m guessing that indexing theory only really works for non-partisan media that follow elite debates, like the NYT? Or does it also work for tabloids? And what about things like Breitbart (and its ilk) .. which I’m guessing appeals explicitly to a populist audience, rather than particularly caring what the elite are talking about?

Matthew: If a study similar to our was done to examine the indexing tendencies of tabloids, Breitbart, or a similar type of media source, the first step would be to determine what is being discussed regularly in these outlets. Assuming, for example, that there isn’t much discussion about marginalized groups in Breitbart, in the context of indexing theory it would not be relevant to examine the pool of congressional Twitter posts mentioning marginalized groups. Those posts are effectively off of Breitbart’s radar. But, generally, indexing theory breaks down if partisanship and bias drive the reporting.

Ed.: Is there any sense in which Trump’s “Twitter diplomacy” has overturned or rendered moot the recent literature on political uses of Twitter? We now have a case where a single (personal) Twitter account can upset the stock market — how does one theorise that?

Matthew: In terms of indexing theory, we could argue that Trump’s Twitter posts themselves generate a response from Democrats and Republicans in Congress and thus muddy the waters by conflating policy issues with other issues like his personality, ties to Russia, his fact-checking problems, etc. This is well beyond our focus in the article, but we speculate that Trump’s early-dawn use of Twitter is primarily for marketing, damage control, and deflection. There are really many different ways to study this phenomenon. One could, for example, examine the function of unfiltered news from politician to the public and compare it with the news that is simultaneously reported in the media. We would also be interested in understanding why Trump and politicians like Trump frame their Twitter posts the way they do, what effect these posts have on their devoted followers as well as their fence-sitting followers, and how this mobilizes Congress both online (i.e. on Twitter) and when discussing and voting on policy options on the Senate and House floors. These areas of research would all build upon rather than render moot the extant literature on the political uses of Twitter.

Ed.: Following on: how does Indexing theory deal with Trump’s populism (i.e. avowedly anti-Washington position), hatred and contempt of the media, and apparent aim of bypassing the mainstream press wherever possible: even ditching the press pool and favouring populist outlets over the NYT in press gaggles. Or is the media bigger than the President .. will indexing theory survive Trump?

Matthew: Indexing theory will of course survive Trump. What we are witnessing in the media is an inability, however, to limit gaper’s block in the sense that the media focus on the more inflammatory and controversial aspects of Trump’s Twitter posts – unfortunately on a daily basis – rather than reporting the policy implications. The media have to report what is news, and Presidential Twitter posts are now newsworthy, but we would argue that we are reaching a point where anything but the meat of the policy implications must be effectively filtered. Until we reach a point where the NYT ignores the inflammatory nature of Trumps Twitter posts, it will be challenging to test indexing theory in the context of the policy agenda setting process.

Ed.: There are recent examples (Brexit, Trump) of the media apparently getting things wrong because they were following the elites and not “the forgotten” (or deplorable) .. who then voted in droves. Is there any sense in the media industry that it needs to rethink things a bit — i.e. that maybe the elite is not always going to be in control of events, or even be an accurate bellwether?

Matthew: This question highlights an omission from our article, namely that indexing theory marginalizes the role of non-elite voices. We agree that the media could do a better job reporting on certain things; for instance, relying extensively on weather vanes of public opinion that do not account for inaccurate self-reporting (i.e. people not accurately representing themselves when being polled about their support for Trump, Brexit, etc.) or understanding why disenfranchised voters might opt to stay home on Election Day. When it comes to setting the policy agenda, which is the focus of our article, we stand by indexing theory given our assumption that the policy process itself is typically directed from those holding power. On that point, and regardless of whether it is normatively appropriate, elites are accurate bellwethers of the policy agenda.

Read the full article: Shapiro, M.A. and Hemphill, L. (2017) Politicians and the Policy Agenda: Does Use of Twitter by the U.S. Congress Direct New York Times Content? Policy & Internet 9 (1) doi:10.1002/poi3.120.

Matthew A. Shapiro and Libby Hemphill were talking to blog editor David Sutcliffe.

Did you consider Twitter’s (lack of) representativeness before doing that predictive study?

Twitter data have many qualities that appeal to researchers. They are extraordinarily easy to collect. They are available in very large quantities. And with a simple 140-character text limit they are easy to analyze. As a result of these attractive qualities, over 1,400 papers have been published using Twitter data, including many attempts to predict disease outbreaks, election results, film box office gross, and stock market movements solely from the content of tweets.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Grant Blank was talking to blog editor David Sutcliffe.

Five Pieces You Should Probably Read On: Fake News and Filter Bubbles

This is the second post in a series that will uncover great writing by faculty and students at the Oxford Internet Institute, things you should probably know, and things that deserve to be brought out for another viewing. This week: Fake News and Filter Bubbles!

Fake news, post-truth, “alternative facts”, filter bubbles — this is the news and media environment we apparently now inhabit, and that has formed the fabric and backdrop of Brexit (“£350 million a week”) and Trump (“This was the largest audience to ever witness an inauguration — period”). Do social media divide us, hide us from each other? Are you particularly aware of what content is personalised for you, what it is you’re not seeing? How much can we do with machine-automated or crowd-sourced verification of facts? And are things really any worse now than when Bacon complained in 1620 about the false notions that “are now in possession of the human understanding, and have taken deep root therein”?

1. Bernie Hogan: How Facebook divides us [Times Literary Supplement]

27 October 2016 / 1000 words / 5 minutes

“Filter bubbles can create an increasingly fractured population, such as the one developing in America. For the many people shocked by the result of the British EU referendum, we can also partially blame filter bubbles: Facebook literally filters our friends’ views that are least palatable to us, yielding a doctored account of their personalities.”

Bernie Hogan says it’s time Facebook considered ways to use the information it has about us to bring us together across political, ideological and cultural lines, rather than hide us from each other or push us into polarized and hostile camps. He says it’s not only possible for Facebook to help mitigate the issues of filter bubbles and context collapse; it’s imperative, and it’s surprisingly simple.

2. Luciano Floridi: Fake news and a 400-year-old problem: we need to resolve the ‘post-truth’ crisis [the Guardian]

29 November 2016 / 1000 words / 5 minutes

“The internet age made big promises to us: a new period of hope and opportunity, connection and empathy, expression and democracy. Yet the digital medium has aged badly because we allowed it to grow chaotically and carelessly, lowering our guard against the deterioration and pollution of our infosphere. […] some of the costs of misinformation may be hard to reverse, especially when confidence and trust are undermined. The tech industry can and must do better to ensure the internet meets its potential to support individuals’ wellbeing and social good.”

The Internet echo chamber satiates our appetite for pleasant lies and reassuring falsehoods, and has become the defining challenge of the 21st century, says Luciano Floridi. So far, the strategy for technology companies has been to deal with the ethical impact of their products retrospectively, but this is not good enough, he says. We need to shape and guide the future of the digital, and stop making it up as we go along. It is time to work on an innovative blueprint for a better kind of infosphere.

3. Philip Howard: Facebook and Twitter’s real sin goes beyond spreading fake news

3 January 2017 / 1000 words / 5 minutes

“With the data at their disposal and the platforms they maintain, social media companies could raise standards for civility by refusing to accept ad revenue for placing fake news. They could let others audit and understand the algorithms that determine who sees what on a platform. Just as important, they could be the platforms for doing better opinion, exit and deliberative polling.”

Only Facebook and Twitter know how pervasive fabricated news stories and misinformation campaigns have become during referendums and elections, says Philip Howard — and allowing fake news and computational propaganda to target specific voters is an act against democratic values. But in a time of weakening polling systems, withholding data about public opinion is actually their major crime against democracy, he says.

4. Brent Mittelstadt: Should there be a better accounting of the algorithms that choose our news for us?

7 December 2016 / 1800 words / 8 minutes

“Transparency is often treated as the solution, but merely opening up algorithms to public and individual scrutiny will not in itself solve the problem. Information about the functionality and effects of personalisation must be meaningful to users if anything is going to be accomplished. At a minimum, users of personalisation systems should be given more information about their blind spots, about the types of information they are not seeing, or where they lie on the map of values or criteria used by the system to tailor content to users.”

A central ideal of democracy is that political discourse should allow a fair and critical exchange of ideas and values. But political discourse is unavoidably mediated by the mechanisms and technologies we use to communicate and receive information, says Brent Mittelstadt. And content personalization systems and the algorithms they rely upon create a new type of curated media that can undermine the fairness and quality of political discourse.

5. Heather Ford: Verification of crowd-sourced information: is this ‘crowd wisdom’ or machine wisdom?

19 November 2013 / 1400 words / 6 minutes

“A key question being asked in the design of future verification mechanisms is the extent to which verification work should be done by humans or non-humans (machines). Here, verification is not a binary categorisation, but rather there is a spectrum between human and non-human verification work, and indeed, projects like Ushahidi, Wikipedia and Galaxy Zoo have all developed different verification mechanisms.”

‘Human’ verification, a process of checking whether a particular report meets a group’s truth standards, is an acutely social process, says Heather Ford. If code is law and if other aspects in addition to code determine how we can act in the world, it is important that we understand the context in which code is deployed. Verification is a practice that determines how we can trust information coming from a variety of sources — only by illuminating such practices and the variety of impacts that code can have in different environments can we begin to understand how code regulates our actions in crowdsourcing environments.

.. and just to prove we’re capable of understanding and acknowledging and assimilating multiple viewpoints on complex things, here’s Helen Margetts, with a different slant on filter bubbles: “Even if political echo chambers were as efficient as some seem to think, there is little evidence that this is what actually shapes election results. After all, by definition echo chambers preach to the converted. It is the undecided people who (for example) the Leave and Trump campaigns needed to reach. And from the research, it looks like they managed to do just that.”

The Authors

Bernie Hogan is a Research Fellow at the OII; his research interests lie at the intersection of social networks and media convergence.

Luciano Floridi is the OII’s Professor of Philosophy and Ethics of Information. His research areas are the philosophy of Information, information and computer ethics, and the philosophy of technology.

Philip Howard is the OII’s Professor of Internet Studies. He investigates the impact of digital media on political life around the world.

Brent Mittelstadt is an OII Postdoc His research interests include the ethics of information handled by medical ICT, theoretical developments in discourse and virtue ethics, and epistemology of information.

Heather Ford completed her doctorate at the OII, where she studied how Wikipedia editors write history as it happens. She is now a University Academic Fellow in Digital Methods at the University of Leeds. Her forthcoming book “Fact Factories: Wikipedia’s Quest for the Sum of All Human Knowledge” will be published by MIT Press.

Helen Margetts is the OII’s Director, and Professor of Society and the Internet. She specialises in digital era government, politics and public policy, and data science and experimental methods. Her most recent book is Political Turbulence (Princeton).

Coming up! .. It’s the economy, stupid / Augmented reality and ambient fun / The platform economy / Power and development / Internet past and future / Government / Labour rights / The disconnected / Ethics / Staying critical

Is Social Media Killing Democracy?

Donald Trump in Reno, Nevada, by Darron Birgenheier (Flickr).

This is the big year for computational propaganda — using immense data sets to manipulate public opinion over social media. Both the Brexit referendum and US election have revealed the limits of modern democracy, and social media platforms are currently setting those limits.

Platforms like Twitter and Facebook now provide a structure for our political lives. We’ve always relied on many kinds of sources for our political news and information. Family, friends, news organizations, charismatic politicians certainly predate the internet. But whereas those are sources of information, social media now provides the structure for political conversation. And the problem is that these technologies permit too much fake news, encourage our herding instincts, and aren’t expected to provide public goods.

First, social algorithms allow fake news stories from untrustworthy sources to spread like wildfire over networks of family and friends. Many of us just assume that there is a modicum of truth-in-advertising. We expect this from advertisements for commercial goods and services, but not from politicians and political parties. Occasionally a political actor gets punished for betraying the public trust through their misinformation campaigns. But in the United States “political speech” is completely free from reasonable public oversight, and in most other countries the media organizations and public offices for watching politicians are legally constrained, poorly financed, or themselves untrustworthy. Research demonstrates that during the campaigns for Brexit and the U.S. presidency, large volumes of fake news stories, false factoids, and absurd claims were passed over social media networks, often by Twitter’s highly automated accounts and Facebook’s algorithms.

Second, social media algorithms provide very real structure to what political scientists often call “elective affinity” or “selective exposure”. When offered the choice of who to spend time with or which organizations to trust, we prefer to strengthen our ties to the people and organizations we already know and like. When offered a choice of news stories, we prefer to read about the issues we already care about, from pundits and news outlets we’ve enjoyed in the past. Random exposure to content is gone from our diets of news and information. The problem is not that we have constructed our own community silos — humans will always do that. The problem is that social media networks take away the random exposure to new, high-quality information.

This is not a technological problem. We are social beings and so we will naturally look for ways to socialize, and we will use technology to socialize each other. But technology could be part of the solution. A not-so-radical redesign might occasionally expose us to new sources of information, or warn us when our own social networks are getting too bounded.

The third problem is that technology companies, including Facebook and Twitter, have been given a “moral pass” on the obligations we hold journalists and civil society groups to.

In most democracies, the public policy and exit polling systems have been broken for a decade. Many social scientists now find that big data, especially network data, does a better job of revealing public preferences than traditional random digit dial systems. So Facebook actually got a moral pass twice this year. Their data on public opinion would have certainly informed the Brexit debate, and their data on voter preferences would certainly have informed public conversation during the US election.

Facebook has run several experiments now, published in scholarly journals, demonstrating that they have the ability to accurately anticipate and measure social trends. Whereas journalists and social scientists feel an obligation to openly analyze and discuss public preferences, we do not expect this of Facebook. The network effects that clearly were unmeasured by pollsters were almost certainly observable to Facebook. When it comes to news and information about politics, or public preferences on important social questions, Facebook has a moral obligation to share data and prevent computational propaganda. The Brexit referendum and US election have taught us that Twitter and Facebook are now media companies. Their engineering decisions are effectively editorial decisions, and we need to expect more openness about how their algorithms work. And we should expect them to deliberate about their editorial decisions.

There are some ways to fix these problems. Opaque software algorithms shape what people find in their news feeds. We’ve all noticed fake news stories (often called clickbait), and while these can be an entertaining part of using the internet, it is bad when they are used to manipulate public opinion. These algorithms work as “bots” on social media platforms like Twitter, where they were used in both the Brexit and US presidential campaign to aggressively advance the case for leaving Europe and the case for electing Trump. Similar algorithms work behind the scenes on Facebook, where they govern what content from your social networks actually gets your attention.

So the first way to strengthen democratic practices is for academics, journalists, policy makers and the interested public to audit social media algorithms. Was Hillary Clinton really replaced by an alien in the final weeks of the 2016 campaign? We all need to be able to see who wrote this story, whether or not it is true, and how it was spread. Most important, Facebook should not allow such stories to be presented as news, much less spread. If they take ad revenue for promoting political misinformation, they should face the same regulatory punishments that a broadcaster would face for doing such a public disservice.

The second problem is a social one that can be exacerbated by information technologies. This means it can also be mitigated by technologies. Introducing random news stories and ensuring exposure to high quality information would be a simple — and healthy — algorithmic adjustment to social media platforms. The third problem could be resolved with moral leadership from within social media firms, but a little public policy oversight from elections officials and media watchdogs would help. Did Facebook see that journalists and pollsters were wrong about public preferences? Facebook should have told us if so, and shared that data.

Social media platforms have provided a structure for spreading around fake news, we users tend to trust our friends and family, and we don’t hold media technology firms accountable for degrading our public conversations. The next big thing for technology evolution is the Internet of Things, which will generate massive amounts of data that will further harden these structures. Is social media damaging democracy? Yes, but we can also use social media to save democracy.

Is Social Media Killing Democracy?

The third problem is that technology companies, including Facebook and Twitter, have been given a “moral pass” on the obligations we hold journalists and civil society groups to.

Rethinking Digital Media and Political Change

Did Twitter lead to Donald Trump’s rise and success to date in the American campaign for the presidency? Image: Gage Skidmore (Flickr)

What are the dangers or new opportunities of digital media? One of the major debates in relation to digital media in the United States has been whether they contribute to political polarization. I argue in a new paper (Rethinking Digital Media and Political Change) that Twitter led to Donald Trump’s rise and success to date in the American campaign for the presidency. There is plenty of evidence to show that Trump received a disproportionate amount of attention on Twitter, which in turn generated a disproportionate amount of attention in the mainstream media. The strong correlation between the two suggests that Trump was able to bypass the gatekeepers of the traditional media.

A second ingredient in his success has been populism, which rails against dominant political elites (including the Republican party) and the ‘biased’ media. Populism also rests on the notion of an ‘authentic’ people — by implication excluding ‘others’ such as immigrants and foreign powers like the Chinese — to whom the leader appeals directly. The paper makes parallels with the strength of the Sweden Democrats, an anti-immigrant party which, in a similar way, has been able to appeal to its following via social media and online newspapers, again bypassing mainstream media with its populist message.

There is a difference, however: in the US, commercial media compete for audience share, so Trump’s controversial tweets have been eagerly embraced by journalists seeking high viewership and readership ratings. In Sweden, where public media dominate and there is far less of the ‘horserace’ politics of American politics, the Sweden Democrats have been more locked out of the mainstream media and of politics. In short, Twitter plus populism has led to Trump. I argue that dominating the mediated attention space is crucial. One outcome of how this story ends will be known in November. But whatever the outcome, it is already clear that the role of the media in politics, and how they can be circumvented by new media, requires fundamental rethinking.

Ralph Schroeder is Professor and director of the Master’s degree in Social Science of the Internet at the Oxford Internet Institute. Before coming to Oxford University, he was Professor in the School of Technology Management and Economics at Chalmers University in Gothenburg (Sweden). Recent books include Rethinking Science, Technology and Social Change (Stanford University Press, 2007) and, co-authored with Eric T. Meyer, Knowledge Machines: Digital Transformations of the Sciences and Humanities (MIT Press 2015).

Digital Disconnect: Parties, Pollsters and Political Analysis in #GE2015

We undertook some live analysis of social media data over the night of the 2015 UK General Election. See more photos from the OII's election night party, or read about the data hack — The Oxford Internet Institute undertook some live analysis of social media data over the night of the 2015 UK General Election. See more photos from the OII’s election night party, or read about the data hack

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.

Use of emergent hashtags on Twitter during the 2015 General Election. Volumes are estimates based on a 10% sample with the exception of #ge2015, which reflects the exact value. All data from Datasift.

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.

Political polarization on social media: do birds of a feather flock together on Twitter?

Twitter has exploded in recent years, now boasting half a billion registered users. Like blogs and the world’s largest social networking platform, Facebook, Twitter has actively been used for political discourse during the past few elections in the US, Canada, and elsewhere but it differs from them in a number of significant ways. Twitter’s connections tend to be less about strong social relationships (such as those between close friends or family members), and more about connecting with people for the purposes of commenting and information sharing. Twitter also provides a steady torrent of updates and resources from individuals, celebrities, media outlets, and any other organization seeking to inform the world as to its views and actions.

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.