Can I read Anomaly Detection with Joint Representation Learning of Content and Connection on EtoBox?

Anomaly Detection with Joint Representation Learning of Content and Connection by Wang, Junhao; Wang, Renhao; Kulshrestha, Aayushi; Rabbany, Reihaneh is a scholarly article available to read on EtoBox.

What is Anomaly Detection with Joint Representation Learning of Content and Connection about?

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.

Author

Wang, Junhao; Wang, Renhao; Kulshrestha, Aayushi; Rabbany, Reihaneh

Published

2019

Language

EN