SCG: Spotting Coordinated Groups in Social Media
This addresses the issue of social media manipulation for political events, offering a tool to spot organized groups, though it is incremental as it builds on existing detection methods by incorporating network analysis.
The study tackled the problem of detecting coordinated groups in social media that manipulate discourse, proposing a method to identify such groups by analyzing user connections and content, and applied it to Twitter data around the 2019 Canadian Federal Elections, finding that users in detected groups were over 4x more likely to get suspended and their hashtags linked to misinformation.
Recent events have led to a burgeoning awareness on the misuse of social media sites to affect political events, sway public opinion, and confuse the voters. Such serious, hostile mass manipulation has motivated a large body of works on bots/troll detection and fake news detection, which mostly focus on classifying at the user level based on the content generated by the users. In this study, we jointly analyze the connections among the users, as well as the content generated by them to Spot Coordinated Groups (SCG), sets of users that are likely to be organized towards impacting the general discourse. Given their tiny size (relative to the whole data), detecting these groups is computationally hard. Our proposed method detects these tiny-clusters effectively and efficiently. We deploy our SCG method to summarize and explain the coordinated groups on Twitter around the 2019 Canadian Federal Elections, by analyzing over 60 thousand user accounts with 3.4 million followership connections, and 1.3 million unique hashtags in the content of their tweets. The users in the detected coordinated groups are over 4x more likely to get suspended, whereas the hashtags which characterize their creed are linked to misinformation campaigns.