Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two-path Bootstrapping Approach
This addresses the problem of detecting hate speech for social media platforms, but it is incremental as it builds on existing weakly supervised techniques.
The authors tackled hate speech detection on social media by proposing a weakly supervised two-path bootstrapping approach, which significantly outperformed supervised methods trained on manually annotated data.
In the wake of a polarizing election, social media is laden with hateful content. To address various limitations of supervised hate speech classification methods including corpus bias and huge cost of annotation, we propose a weakly supervised two-path bootstrapping approach for an online hate speech detection model leveraging large-scale unlabeled data. This system significantly outperforms hate speech detection systems that are trained in a supervised manner using manually annotated data. Applying this model on a large quantity of tweets collected before, after, and on election day reveals motivations and patterns of inflammatory language.