A Unified Multi-Task Learning Architecture for Hate Detection Leveraging User-Based Information
This addresses the problem of filtering hate content at scale for social media platforms, but it is incremental as it builds on existing deep learning methods with user-based features.
The paper tackles hate speech detection in social media by introducing a model that uses intra-user and inter-user information, showing significant improvements in macro-F1 and weighted-F1 scores on benchmark datasets.
Hate speech, offensive language, aggression, racism, sexism, and other abusive language are common phenomena in social media. There is a need for Artificial Intelligence(AI)based intervention which can filter hate content at scale. Most existing hate speech detection solutions have utilized the features by treating each post as an isolated input instance for the classification. This paper addresses this issue by introducing a unique model that improves hate speech identification for the English language by utilising intra-user and inter-user-based information. The experiment is conducted over single-task learning (STL) and multi-task learning (MTL) paradigms that use deep neural networks, such as convolutional neural networks (CNN), gated recurrent unit (GRU), bidirectional encoder representations from the transformer (BERT), and A Lite BERT (ALBERT). We use three benchmark datasets and conclude that combining certain user features with textual features gives significant improvements in macro-F1 and weighted-F1.