Over the past few years, extensive anecdotal evidence emerged that suggests the involvement of state-sponsored actors (or "trolls") in online political campaigns with the goal to manipulate public opinion and sow discord. Recently, Twitter and Reddit released ground truth data about Russian and Iranian state-sponsored actors that were active on their platforms. In this paper, we analyze these ground truth datasets across several axes to understand how these actors operate, how they evolve over time, who are their targets, how their strategies changed over time, and what is their influence to the Web's information ecosystem. Among other things we find: a) campaigns of these actors were influenced by real-world events; b) these actors were employing different tactics and had different targets over time, thus their automated detection is not straightforward; and c) Russian trolls were clearly pro-Trump, whereas Iranian trolls were anti-Trump. Finally, using Hawkes Processes, we quantified the influence that these actors had to four Web communities: Reddit, Twitter, 4chan's Politically Incorrect board ( pol ), and Gab, finding that Russian trolls were more influential than Iranians with the exception of pol .
We now review previous work on opinion manipulation as well as politically motivated disinformation on the Web. Opinion manipulation. The practice of swaying opinion in Web communities has become a hot-button issue as malicious actors are intensifying their efforts to push their subversive agenda. @cite_36 study how users create multiple accounts, called , that actively participate in some communities with the goal to manipulate users' opinions. @cite_34 show that trolls can indeed manipulate users' opinions in online forums. In follow-up work, Mihaylov and Nakov @cite_25 highlight two types of trolls: those paid to operate and those that are called out as such by other users. Then, Volkova and Bell @cite_15 aim to predict the deletion of Twitter accounts because they are trolls, focusing on those that shared content related to the Russia-Ukraine crisis.
@cite_36 @cite_15 @cite_34 @cite_25
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Over the past couple of years, anecdotal evidence has emerged linking coordinated campaigns by state-sponsored actors with efforts to manipulate public opinion on the Web, often around major political events, through dedicated accounts, or "trolls." Although they are often involved in spreading disinformation on social media, there is little understanding of how these trolls operate, what type of content they disseminate, and most importantly their influence on the information ecosystem. In this paper, we shed light on these questions by analyzing 27K tweets posted by 1K Twitter users identified as having ties with Russia's Internet Research Agency and thus likely state-sponsored trolls. We compare their behavior to a random set of Twitter users, finding interesting differences in terms of the content they disseminate, the evolution of their account, as well as their general behavior and use of the Twitter platform. Then, using a statistical model known as Hawkes Processes, we quantify the influence that these accounts had on the dissemination of news on social platforms such as Twitter, Reddit, and 4chan. Overall, our findings indicate that Russian troll accounts managed to stay active for long periods of time and to reach a substantial number of Twitter users with their messages. When looking at their ability of spreading news content and making it viral, however, we find that their effect on social platforms was minor, with the significant exception of news published by the Russian state-sponsored news outlet RT (Russia Today). Social media, once hailed as a vehicle for democratization and the promotion of positive social change across the globe, are under attack for becoming a tool of political manipulation and spread of disinformation. A case in point is the alleged use of trolls by Russia to spread malicious content in Western elections. This paper examines the Russian interference campaign in the 2016 US presidential election on Twitter. Our aim is twofold: first, we test whether predicting users who spread trolls' content is feasible in order to gain insight on how to contain their influence in the future; second, we identify features that are most predictive of users who either intentionally or unintentionally play a vital role in spreading this malicious content. We collected a dataset with over 43 million elections-related posts shared on Twitter between September 16 and November 9, 2016, by about 5.7 million users. This dataset includes accounts associated with the Russian trolls identified by the US Congress. Proposed models are able to very accurately identify users who spread the trolls' content (average AUC score of 96 , using 10-fold validation). We show that political ideology, bot likelihood scores, and some activity-related account meta data are the most predictive features of whether a user spreads trolls' content or not. Over the past few years, extensive anecdotal evidence emerged that suggests the involvement of state-sponsored actors (or "trolls") in online political campaigns with the goal to manipulate public opinion and sow discord. Recently, Twitter and Reddit released ground truth data about Russian and Iranian state-sponsored actors that were active on their platforms. In this paper, we analyze these ground truth datasets across several axes to understand how these actors operate, how they evolve over time, who are their targets, how their strategies changed over time, and what is their influence to the Web's information ecosystem. Among other things we find: a) campaigns of these actors were influenced by real-world events; b) these actors were employing different tactics and had different targets over time, thus their automated detection is not straightforward; and c) Russian trolls were clearly pro-Trump, whereas Iranian trolls were anti-Trump. Finally, using Hawkes Processes, we quantified the influence that these actors had to four Web communities: Reddit, Twitter, 4chan's Politically Incorrect board ( pol ), and Gab, finding that Russian trolls were more influential than Iranians with the exception of pol . The emergence of user forums in electronic news media has given rise to the proliferation of opinion manipulation trolls. Finding such trolls automatically is a hard task, as there is no easy way to recognize or even to define what they are; this also makes it hard to get training and testing data. We solve this issue pragmatically: we assume that a user who is called a troll by several people is likely to be one. We experiment with different variations of this definition, and in each case we show that we can train a classifier to distinguish a likely troll from a non-troll with very high accuracy, 82‐95 , thanks to our rich feature set.