Peer Reviewed Publications
2017. "Social Media and EuroMaidan: A Review Essay", with Joshua Tucker. Slavic Review. 76(1): 169-191. (link)
As more than a billion people had done previously, on November 21, 2013, Ukrainian journalist and activist Mustafa Nayem wrote a Facebook post; this post, however, would have a much larger impact on subsequent political developments than most that had preceded it. Frustrated with President Viktor Yanukovych’s decision not to sign a long-promised association agreement with the European Union, Nayem asked others who shared his frustration to comment on his post. Even more importantly, Nayem wrote that if the post received at least 1000 comments from people willing to join him, they should all go to Independence Square to protest. And indeed they did: starting with just a few thousand people, the protests would swell to be the largest since Ukraine’s independence, particularly after police used force against protesters at the end of November 2013. Eventually, these protests led to the resignation of the government, the exile of the former president, and indirectly to the secession of Crimea and the ongoing conflict in the eastern part of the country.
2016. “Tweeting Identity? Ukrainian, Russian and #EuroMaidan”, with Joshua Tucker, Jonathan Nagler, and Richard Bonneau. Journal of Comparative Economics. 44(1): 16-40. (link)
Why and when do group identities become salient? Existing scholarship has suggested that insecurity and competition over political and economic resources as well as increased perceptions of threat from the out-group tend to increase the salience of ethnic identities. Most of the work on ethnicity, however, is either experimental and deals with how people respond once identity has already been primed, is based on self-reported measures of identity, or driven by election results. In contrast, here we examine events in Ukraine from late 2013 (the beginning of the Euromaidan protests) through the end of 2014 to see if particular moments of heightened political tension led to increased identification as either “Russian” or “Ukrainian” among Ukrainian citizens. In tackling this question, we use a novel methodological approach by testing the hypothesis that those who prefer to use Ukrainian to communicate on Twitter will use Ukrainian (at the expense of Russian) following moments of heightened political awareness and those who prefer to use Russian will do the opposite. Interestingly, our primary finding in is a negative result: we do not find evidence that key political events in the Ukrainian crisis led to a reversion to the language of choice at the aggregate level, which is interesting given how much ink has been spilt on the question of the extent to which Euromaidan reflected an underlying Ukrainian vs. Russian conflict. However, we unexpectedly find that both those who prefer Russian and those who prefer Ukrainian begin using Russian with a greater frequency following the annexation of Crimea, thus contributing a whole new set of puzzles – and a method for exploring these puzzles – that can serve as a basis for future research.
2015. "Big Data, Social Media and Protest: Foundations for a Research Agenda", with Joshua Tucker, Jonathan Nagler, Pablo Barbera, Duncan Penfold-Brown and Richard Bonneau. In Computational Social Science (edited by Michael Alvarez). Cambridge University Press. (link)
"Social Media Networks, Non-Local Actors and Information Transmission During Protest: Evidence from Ukraine’s EuroMaidan" (link)
This paper explores one specific way this new tool may have shaped information exchange: by changing who can exchange information widely during protest. We know from previous research (Bruns, Highfield and Burgess, 2013; Eltantawy and Wiest, 2011) that non-locals sometimes represent a significant portion of the people exchanging information about protest online, but what is their role, and what does their presence change about how information is transmitted during protest and about the informational environment as a whole? What are the implications of these changes for the dynamics of protest? To study this question, I use a dataset of every tweet sent containing over 30 keywords and hashtags during the EuroMaidan protests in Ukraine in 2013-2014. I find that while non-locals make up a significant proportion of those discussing protests online, interactions between geolocated locals and non-locals are relatively minimal. Non-locals, however, are sharing, and amplifying the visibility, of very different types of information than locals. Particularly, an enormous amount of the most shared information among non-locals comes from Russian funded sources, with an informational agenda that differs substantially from the Ukrainian media or from the agenda of the protest groups themselves. My findings suggest that social media creates space for new people to become important disseminators of information around protest, but the implications of this can be both positive and negative from the perspective of protest movements themselves. On the one hand, protestors have more opportunities to correct inaccurate information, to disseminate their own perspectives, and to craft their own narratives, as well as to mobilize resources from a broader set of individuals. On the other hand, social media also provides opportunities for groups with competing agendas to become more important and influential in informational networks around protest than they might be in other contexts.
"Digital Media and Protest: Impacts on the Dynamics of Protest Emergence and State Response" (link)
In 2015, a Pew study found that in the United States 1 in 10 adults got news on Twitter and 1 in 4 got news on Facebook. These numbers are even higher for young adults. In just over ten years, then, we have gone from social media not existing to being a critical source of news and information. This paper examines the impact that this informational shift has had on a particular type of political behavior: political protest, particularly large-scale anti-government protest. The key question that I attempt to answer is what the overall impact of social media has been on how often protests emerge, what types of protests emerge, and how likely they are to succeed. Recent scholarship has speculated about the impacts that social media might have on the likelihood of protest emergence, or on the type of protests that might emerge, and has ranged from the utopic to the dystopic. Using global data on protest, I test a variety of hypotheses about what the impact of social media on protest might be, and how the introduction of this technology has (or has not) changed the dynamics of protest emergence.
"Dynamics of Influence in Online Protest Networks: Evidence from the 2013 Turkish Protests" (link)
The dynamics of information diffusion are a key element of many theories of protest emergence and success. Protest itself can serve as a public signal of preferences, but theory suggests that individuals need significant signals from others before being willing to participate in such high risk action. Thus the spread of information on levels of dissent and participation in protest contexts is critical, as is the creation of common knowledge about this information. Given the importance of information in these contexts, understanding who is able to spread information effectively and how is critical. Those whose information spreads most widely play a stronger role in shaping how protest events evolve. During protest, this information impacts the way that people make choices about participation, the way the protests are portrayed in the public eye, and what information becomes widely available. Previously, we were limited in our ability to study these dynamics effectively, because we were unable to observe the communication networks that underlie protest organization and participation. Social media data provides exciting new opportunities to explore this question in depth. In this paper, I draw on a new data set of over 30 million tweets gathered during the Gezi Park Protests in Istanbul in the summer of 2013 to study the dynamics of influence in online protest networks. My findings suggest that network centrality is a necessary, but not sufficient condition for influence in online protest networks. Rather, other factors contribute to influence, but they must be paired with a critical mass of followers to actually allow an individual to emerge as influential. I further find conflicting evidence about whether the type of content in tweets of highly retweeted users differs from that of other users, and suggest questions for future research