A Trolling Hierarchy in Social Media and A Conditional Random Field For Trolling Detection
This work addresses the issue of malicious interactions in social media for platform moderators and researchers, but is incremental as it builds on existing politeness research and focuses on categorization.
The paper tackled the problem of detecting and categorizing trolling behavior in online conversations by proposing a model that predicts four key aspects of trolling, and introduced a new annotated dataset for research.
An-ever increasing number of social media websites, electronic newspapers and Internet forums allow visitors to leave comments for others to read and interact. This exchange is not free from participants with malicious intentions, which do not contribute with the written conversation. Among different communities users adopt strategies to handle such users. In this paper we present a comprehensive categorization of the trolling phenomena resource, inspired by politeness research and propose a model that jointly predicts four crucial aspects of trolling: intention, interpretation, intention disclosure and response strategy. Finally, we present a new annotated dataset containing excerpts of conversations involving trolls and the interactions with other users that we hope will be a useful resource for the research community.