Defining and predicting troll vulnerability in online social media

Trolling describes a range of antisocial online behaviors that aim at disrupting the normal operation of online social networks and media. Existing approaches to combating trolling rely on human-based or automatic mechanisms for identifying trolls and troll posts. In this paper, we take a novel approach to the problem: our goal is to identify troll vulnerable posts, that is, posts that are potential targets of trolls, so as to prevent trolling before it happens. To this end, we define three natural axioms that a troll vulnerability metric must satisfy and introduce metrics that satisfy them. We then define the troll vulnerability prediction problem, where given a post we aim at predicting whether it is vulnerable to trolling. We construct models that use features from the content and the history of the post for the prediction. Our experiments with real data from Reddit demonstrate that our approach is successful in identifying a large fraction of the troll vulnerable posts.