Keywords: twitter, trolls, coordinated accounts, misinformation, networks, social media, social networks, link prediction, node classification
TL;DR: A set of experiments performed on a Twitter troll dataset to learn their behaviour and classify them from real accounts.
Abstract: In modern days, social media platforms provide accessible channels for the inter-action and immediate reflection of the most important events happening around the world. In this paper, we, firstly, present a curated set of datasets whose origin stem from the Twitter’s Information Operations efforts. More notably, these accounts, which have been already suspended, provide a notion of how state-backed human trolls operate.Secondly, we present detailed analyses of how these behaviours vary over time,and motivate its use and abstraction in the context of deep representation learning:for instance, to learn and, potentially track, troll behaviour. We present baselinesf or such tasks and highlight the differences there may exist within the literature.Finally, we utilize the representations learned for behaviour prediction to classify trolls from"real"users, using a sample of non-suspended active accounts.