  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.
