Work in Progress
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For Whom the Bot Tolls: A Neural Networks Approach to Measuring Political Orientation of Twitter Bots in Russia
(Co-authored with Sergey Sanovich, Joshua A. Tucker, and Rich Bonneau) Under review Computational propaganda and the use of automated accounts in social media have recently become the focus of public attention, with alleged Russian government activities abroad provoking particularly widespread interest. However, even in the Russian domestic context, where anecdotal evidence of state activity predates current activities by almost a decade, no public systematic attempt has been made to dissect the population of Russian social media bots by their political orientation. We address this gap by developing a deep neural network classifier that separates pro-regime, anti-regime, and neutral Russian Twitter bots. Our method relies on supervised machine learning and a new large set of labeled accounts described here, rather than externally obtained account affiliations or orientation of elites. We also illustrate the use of our method by applying it to bots operating in Russian political Twitter from 2015 -- 2017 and show that both pro- and anti-Kremlin bots had a substantial presence on Twitter. |
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Recent research on the role of quasi-democratic institutions in non-democratic regimes finds differential effects of legislatures on economic performance and regime stability across subtypes of non-democracy. This empirically observed variation has however remained a "black box'" lacking a micro-theoretical foundation.
This paper helps bridge this gap by proposing a theory of two types of non-ruling parties (real and decoy opposition) that might be present in a non-democratic legislature and affect the way the legislature functions. I test behavioral implications of the theory using parliamentary speeches from the Russian legislature under Putin and show that, unlike decoy parties, real opposition strives to set the agenda on the floor of the parliament. I also show that decoy parties, unlike real opposition, avoid raising topics about the foundations of the regime, although they are trying to deceive voters by discussing weak points of the government. This variation in parties' strategies helps shed new light on the internal functioning of an autocratic legislature. |
Source: http://www.jantoo.com/
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Recent Publications
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How Autocrats Manipulate Economic News: Evidence from Russia's State-Controlled Television
(Co-authored with Arturas Rozenas) Forthcoming in Journal of Politics Conventional wisdom says that autocrats manipulate news through censorship. We argue that when it comes to economic affairs, the state's ability to censor information effectively is limited. Instead of censoring economic facts, the media tactically frames those facts to make the government appear as a competent economic manager. Using a corpus of daily news reports from Russia's largest state-owned television network, we document extensive evidence supporting this argument: bad news is not censored, but it is systematically blamed on external factors, whereas good news is systematically attributed to domestic politicians. This selective attribution is used to improve the government's reputation in politically sensitive times, but it is also employed as a pandering tool by the media. |
Source: http://cccp.narod.ru/graph/foto/plakat/staliniana/radost.jpg
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Source: https://fortypercentjourney.files.wordpress.com
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Turning the Virtual Tables: Government Strategies for Addressing Online Opposition in Russia (Co-authored with Sergey Sanovich and Joshua A. Tucker) (2018) Comparative Politics 50(3): 435-482 Even though Internet and social media have been recently dubbed "liberation technology", there is growing evidence that non-democratic regimes have learned to use these tools for their own purposes including public opinion manipulation and agenda setting.
We introduce a novel classification of strategies employed by autocrats to combat online opposition generally, and opposition on social media in particular. We further illustrate different strategies with evidence from Russia since 2000. In addition, for the strategy that we call online engagement we construct the tools for detecting such activity on Twitter and test them on a large dataset of politically relevant Twitter data from Russia, gathered over a year and a half. We make preliminary conclusions about the prevalence of "bots'' in the Russian Twittersphere. |
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Detecting Bots on Russian Political Twitter
(Co-authored with Sergey Sanovich, Richard Bonneau, and Joshua A. Tucker) (2017) Big Data 5(4): 310-324 Automated and semiautomated Twitter accounts, bots, have recently gained significant public attention due to their potential interference in the political realm. In this study, we develop a methodology for detecting bots on Twitter using an ensemble of classifiers and apply it to study bot activity within political discussions in the Russian Twittersphere. We focus on the interval from February 2014 to December 2015, an especially consequential period in Russian politics. Among accounts actively tweeting about Russian politics, we find that on the majority of days, the proportion of tweets produced by bots exceeds 50%. We reveal bot characteristics that distinguish them from humans in this corpus, and find that the software platform used for tweeting is among the best predictors of bots. Finally, we find suggestive evidence that one prominent activity that bots were involved in on Russian political Twitter is the spread of news stories and promotion of media who produce them. Supplementary materials: Twitter snapshot example |
Source: http://www.jjrobots.com/wp-content/uploads/2015/06/robot-evolution-GIF.gif
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Source: http://www.e-reading.club/
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"King of the Mountain," Or Why Postcommunist Autocracies Have Bad Institutions
(2014) Russian Politics and Law 52 (2): 7-29 Co-authored with Andrei Melville and Mikhail Mironyuk We reconsider the relationship between democracy and state capacity in post-communist countries over the past two decades and question the general validity of the J-curve hypothesis. We also present informally our "king of the mountain" model explaining why autocrats have bad institutions in post-communist countries.
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Trajectories of Regime Transformation and Types of Stateness in Post-Communist Countries
(2014) Perspectives on European Politics and Society 14 (4): 431-459 Co-authored with Andrei Melville and Mikhail Mironyuk What types of factors are crucial for regime transformation in post-communist countries? We consider both structural and actor-oriented factors and find that the latter do a better job in explaining regime outcomes. We also examine post-communist stateness as another possible explanation for differences in regime transformations. We propose a vector measure of stateness which allows us to build a typology of stateness in post-communist countries that reveals the relationship between regime and stateness dynamics. We find that those post-communist states which fulfill a broad range of social functions are more successful in their democratic development.
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