Characterizing the 2016 Russian IRA influence campaign

• Published in: Social network analysis and mining 2019-12, Vol.9 (1), p.31, Article 31
• Main Authors: Badawy, Adam, Addawood, Aseel, Lerman, Kristina, Ferrara, Emilio
• Format: Article
• Language: English
• Subjects: Accounts; Applications of Graph Theory and Complex Networks; Computer Science; Conservatism; Data Mining and Knowledge Discovery; Datasets; Demonstrations & protests; Economics; Elections; False information; Game Theory; Humanities; Ideology; Islam; Law; Machine learning; Manipulation; Mass media; Methodology of the Social Sciences; News; Original Article; Political campaigns; Politics; Presidential elections; Propaganda; Public opinion; Refugees; Schools; Scrutiny; Social and Behav. Sciences; Social media; Social networks; Software agents; Statistics for Social Sciences; Terrorism; Text analysis; Verbal communication
• ISSN: 1869-5450; 1869-5469
• DOI: 10.1007/s13278-019-0578-6

Until recently, social media were seen to promote democratic discourse on social and political issues. However, this powerful communication ecosystem has come under scrutiny for allowing hostile actors to exploit online discussions in an attempt to manipulate public opinion. A case in point is the ongoing U.S. Congress investigation of Russian interference in the 2016 U.S. election campaign, with Russia accused of, among other things, using trolls (malicious accounts created for the purpose of manipulation) and bots (automated accounts) to spread propaganda and politically biased information. In this study, we explore the effects of this manipulation campaign, taking a closer look at users who re-shared the posts produced on Twitter by the Russian troll accounts publicly disclosed by U.S. Congress investigation. We collected a dataset of 13 million election-related posts shared on Twitter in the year of 2016 by over a million distinct users. This dataset includes accounts associated with the identified Russian trolls as well as users sharing posts in the same time period on a variety of topics around the 2016 elections. We use label propagation to infer the users’ ideology based on the news sources they share. We are able to classify a large number of the users as liberal or conservative with precision and recall above 84%. Conservative users who retweet Russian trolls produced significantly more tweets than liberal ones, about 8 times as many in terms of tweets. Additionally, trolls’ position in the retweet network is stable overtime, unlike users who retweet them who form the core of the election-related retweet network by the end of 2016. Using state-of-the-art bot detection techniques, we estimate that about 5% and 11% of liberal and conservative users are bots, respectively. Text analysis on the content shared by trolls reveal that conservative trolls talk about refugees, terrorism, and Islam, while liberal trolls talk more about school shootings and the police. Although an ideologically broad swath of Twitter users were exposed to Russian trolls in the period leading up to the 2016 U.S. Presidential election, it is mainly conservatives who help amplify their message.