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.

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

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

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

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

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

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

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

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

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

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

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

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