Antisocial Behavior on the Web: Characterization and Detection

Web platforms enable unprecedented breadth and speed in transmission of knowledge, and allow users to communicate and shape opinions. However, the safety, usability and reliability of these platforms are compromised by the prevalence of online antisocial behavior, for e.g., 40% of users have experienced online harassment [3]. Antisocial behavior is present in the form of antisocial users, such as trolls, sockpuppets and vandals, and misinformation, such as hoaxes, rumors and fraudulent reviews [37]. This tutorial presents the state-of-the-art research spanning two aspects of antisocial behavior: characterization of their behavioral properties, and development of algorithms for identifying and predicting them. The tutorial first discusses antisocial users — trolls, sockpuppets and vandals. We present the causes, community effects, and linguistic, social and temporal characteristics of trolls. Then we discuss the types of sockpuppets, i.e. multiple accounts of the same user, and their behavioral characteristics in Wikipedia and online discussion forums. Vandals make destructive edits on Wikipedia and we discuss the properties of vandals and vandalism edits. In each case, detection and prediction algorithms of the antisocial user are also discussed. The second part of the tutorial discusses misinformation — hoaxes, rumors and fraudulent reviews. We present the characteristics and impact of hoaxes on Wikipedia, followed by the spread and evolution of rumors on social media. Then, we discuss the algorithms to identify fake reviews and reviewers from their characteristics, and the camouflage and coordination among sophisticated fraudsters. Again, in each case, we present the detection algorithms, using textual, temporal, sentiment, network structure and rating patterns. Finally, the tutorial concludes with future research avenues.