Author Profiling for Hate Speech Detection
This addresses the problem of detecting hate speech online for social media platforms and users, but it is incremental as it builds on existing methods by adding user profiling.
The paper tackled hate speech detection on social media by incorporating community-based profiling features of Twitter users, showing that their methods significantly outperform the current state of the art on a dataset of 16k tweets.
The rapid growth of social media in recent years has fed into some highly undesirable phenomena such as proliferation of abusive and offensive language on the Internet. Previous research suggests that such hateful content tends to come from users who share a set of common stereotypes and form communities around them. The current state-of-the-art approaches to hate speech detection are oblivious to user and community information and rely entirely on textual (i.e., lexical and semantic) cues. In this paper, we propose a novel approach to this problem that incorporates community-based profiling features of Twitter users. Experimenting with a dataset of 16k tweets, we show that our methods significantly outperform the current state of the art in hate speech detection. Further, we conduct a qualitative analysis of model characteristics. We release our code, pre-trained models and all the resources used in the public domain.