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 .
Howard and Kollanyi @cite_42 study the role of bots in Twitter conversations during the 2016 Brexit referendum. They find that most tweets are in favor of Brexit, that there are bots with various levels of automation, and that 1 , Hegelich and Janetzko @cite_4 investigate whether bots on Twitter are used as political actors. By exposing and analyzing 1.7K bots on Twitter during the Russia-Ukraine conflict, they uncover their political agenda and show that bots exhibit various behaviors, e.g., trying to hide their identity, promoting topics through the use of hashtags, and retweeting messages with particularly interesting content. @cite_18 aim to predict users that are likely to spread information from state-sponsored actors, while @cite_14 focus on the Facebook platform and analyze ads shared by Russian trolls in order to find the cues that make them effective. Finally, a large body of work focuses on social bots @cite_6 @cite_46 @cite_1 @cite_20 @cite_28 and their role in spreading political disinformation, highlighting that they can manipulate the public's opinion at a large scale, thus potentially affecting the outcome of political elections.
@cite_18 @cite_14 @cite_4 @cite_28 @cite_42 @cite_1 @cite_6 @cite_46 @cite_20
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Bots are social media accounts that automate interaction with other users, and they are active on the StrongerIn-Brexit conversation happening over Twitter. These automated scripts generate content through these platforms and then interact with people. Political bots are automated accounts that are particularly active on public policy issues, elections, and political crises. In this preliminary study on the use of political bots during the UK referendum on EU membership, we analyze the tweeting patterns for both human users and bots. We find that political bots have a small but strategic role in the referendum conversations: (1) the family of hashtags associated with the argument for leaving the EU dominates, (2) different perspectives on the issue utilize different levels of automation, and (3) less than 1 percent of sampled accounts generate almost a third of all the messages. Recent accounts from researchers, journalists, as well as federal investigators, reached a unanimous conclusion: social media are systematically exploited to manipulate and alter public opinion. Some disinformation campaigns have been coordinated by means of bots, social media accounts controlled by computer scripts that try to disguise themselves as legitimate human users. In this study, we describe one such operation that occurred in the run up to the 2017 French presidential election. We collected a massive Twitter dataset of nearly 17 million posts, posted between 27 April and 7 May 2017 (Election Day). We then set to study the MacronLeaks disinformation campaign: By leveraging a mix of machine learning and cognitive behavioral modeling techniques, we separated humans from bots, and then studied the activities of the two groups independently, as well as their interplay. We provide a characterization of both the bots and the users who engaged with them, and oppose it to those users who didn’t. Prior interests of disinformation adopters pinpoint to the reasons of scarce success of this campaign: the users who engaged with MacronLeaks are mostly foreigners with pre-existing interest in alt-right topics and alternative news media, rather than French users with diverse political views. Concluding, anomalous account usage patterns suggest the possible existence of a black market for reusable political disinformation bots. 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. AbstractBrexit i.e. “British Exit” is one of the major events in the history of economics and British politics. When EU referendum took place, 52 of votes were in favor of United Kingdom leaving the European Union. This was a major event affecting the overall economy of Britain and was also one of the most talked about events. Twitter is a social network platform where users from all over the world discussed a lot about Brexit and British politics. Analysis of such user generated text can reveal a lot about the event and what people think of it. Sentiment analysis is a trending technique to get insights from any written text. Our aim in this study is to analyze the tweets geospatially and then perform Geo-spatial Sentiment analysis based on the location of the events verses the geospatial tweets distribution for that particular event on global level. Also shows the keywords and hashtags were mostly used by people during that time and how they were being used i.e. which hashtag was used positively, which ...