  Social media sites are becoming a key factor in politics. These platforms are
easy to manipulate for the purpose of distorting information space to confuse
and distract voters. Past works to identify disruptive patterns are mostly
focused on analyzing the content of tweets. In this study, we jointly embed the
information from both user posted content as well as a user's follower network,
to detect groups of densely connected users in an unsupervised fashion. We then
investigate these dense sub-blocks of users to flag anomalous behavior. In our
experiments, we study the tweets related to the upcoming 2019 Canadian
Elections, and observe a set of densely-connected users engaging in local
politics in different provinces, and exhibiting troll-like behavior.
