Predicting the Role of Political Trolls in Social Media

Proceedings of the 23rd Conference on Computational Natural Language Learning, pages 1023–1034 Hong Kong, China, November 3-4, 2019. c⃝2019 Association for Computational Linguistics 1023 Predicting the Role of Political Trolls in Social Media Atanas Atanasov Sofia University Bulgaria amitkov@uni-sofia.bg Gianmarco De Francisci Morales ISI Foundation Italy gdfm@acm.org Preslav Nakov Qatar Computing Research Institute, HBKU, Qatar pnakov@qf.org.qa Abstract We investigate the political roles of “Inter- net trolls” in social media. Political trolls, such as the ones linked to the Russian In- ternet Research Agency (IRA), have recently gained enormous attention for their ability to sway public opinion and even influence elections. Analysis of the online traces of trolls has shown different behavioral patterns, which target different slices of the population. However, this analysis is manual and labor- intensive, thus making it impractical as a first- response tool for newly-discovered troll farms. In this paper, we show how to automate this analysis by using machine learning in a real- istic setting. In particular, we show how to classify trolls according to their political role —left, news feed, right— by using features extracted from social media, i.e., Twitter, in two scenarios: (i) in a traditional supervised learning scenario, where labels for trolls are available, and (ii) in a distant supervision sce- nario, where labels for trolls are not available, and we rely on more-commonly-available la- bels for news outlets mentioned by the trolls. Technically, we leverage the community struc- ture and the text of the messages in the on- line social network of trolls represented as a graph, from which we extract several types of learned representations, i.e., embeddings, for the trolls. Experiments on the “IRA Russian Troll” dataset show that our methodology im- proves over the state-of-the-art in the first sce- nario, while providing a compelling case for the second scenario, which has not been ex- plored in the literature thus far. 1 Introduction Internet “trolls” are users of an online community who quarrel and upset people, seeking to sow dis- cord by posting inflammatory content. More re- cently, organized “troll farms” of political opinion manipulation trolls have also emerged. Such farms usually consist of state-sponsored agents who control a set of pseudonymous user accounts and personas, the so-called “sockpup- pets”, which disseminate misinformation and pro- paganda in order to sway opinions, destabilize the society, and even influence elections (Linvill and Warren, 2018). The behavior of political trolls has been ana- lyzed in different recent circumstances, such as the 2016 US Presidential Elections and the Brexit referendum in UK (Linvill and Warren, 2018; Llewellyn et al., 2018). However, this kind of analysis requires painstaking and time-consuming manual labor to sift through the data and to catego- rize the trolls according to their actions. Our goal in the current paper is to automate this process with the help of machine learning (ML). In par- ticular, we focus on the case of the 2016 US Pres- idential Elections, for which a public dataset from Twitter is available. For this case, we consider only accounts …

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