Troll vulnerability in online social networks
Paraskevas Tsantarliotis, Evaggelia Pitoura, Panayiotis Tsaparas · 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · 2016
Trolling describes a range of antisocial online behaviors that aim at disrupting the normal operation of online social networks and media. Combating trolling is an important problem in the online world. Existing approaches rely on human-based or automatic mechanisms for identifying trolls and troll posts. In this paper we take a novel approach to the trolling problem: our goal is to identify the targets of the trolls, so as to prevent trolling before it happens. We thus define the troll vulnerability prediction problem, where given a post we aim at predicting whether it is vulnerable to trolling. Towards this end, we define a novel troll vulnerability metric of how likely a post is to be attacked by trolls, and we construct models for predicting troll-vulnerable posts, using features from the content and the history of the post. Our experiments with real data from Reddit demonstrate that our approach is successful in recalling a large fraction of the troll-vulnerable posts.