SCG: Spotting Coordinated Groups in Social Media

TL;DR

This paper introduces a method to detect small, organized groups on social media that influence discourse, by analyzing user connections and content, demonstrated on Twitter data from the 2019 Canadian elections.

Contribution

The study presents a novel approach for identifying coordinated groups by jointly analyzing user connections and content, addressing the challenge of detecting tiny clusters efficiently.

Findings

Detected groups are over 4 times more likely to be suspended.

Characterized hashtags linked to misinformation campaigns.

Effective analysis of large-scale Twitter data.

Abstract

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.