Half-Day Tutorial on Combating Online Hate Speech: The Role of Content, Networks, Psychology, User Behavior, etc.

Abstract

While the rise in popularity of social media is seen as a hugely positive development, it is also accompanied by a proliferation of hate speech, which has recently become a major concern. On the one hand, hateful content creates an unsafe environment for certain members of society. On the other hand, manual moderation causes distress to content moderators, and the volume of harmful content is far beyond what human moderators can manually flag and react to. Thus, researchers in machine learning, social computing, and other areas have worked on developing tools to help automate the process. While initially studied as a text classification problem, over time, researchers realized that hate speech is multi-faceted and requires analysis of the role of linguistic expressions, context, and network structure, while using inspiration from psychology and user behavior, among others. With this in mind, we provide a holistic view of what the research community has explored so far, and what we believe are promising future research directions.