Anecdotal evidence has emerged suggesting that state-sponsored organizations, like the Russian Internet Research Agency, have exploited mainstream social. Their primary goal is apparently to conduct information warfare operations to manipulate public opinion using accounts disguised as "normal" people. To increase engagement and credibility of their posts, these accounts regularly share images. However, the use of images by state-sponsored accounts has yet to be examined by the research community. In this work, we address this gap by analyzing a ground truth dataset of 1.8M images posted to Twitter by called Russian trolls. More specifically, we analyze the content of the images, as well as the posting activity of the accounts. Among other things, we find that image posting activity of Russian trolls is tightly coupled with real-world events, and that their targets, as well as the content shared, changed over time. When looking at the interplay between domains that shared the same images as state-sponsored trolls, we find clear cut differences in the origin and or spread of images across the Web. Overall, our findings provide new insight into how state-sponsored trolls operate, and specifically how they use imagery to achieve their goals.
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.