This paper presents a unified user profiling framework to identify hate speech spreaders by processing their tweets regardless of the language. The framework encodes the tweets with sentence transformers and applies an attention mechanism to select important tweets for learning user profiles. Furthermore, the attention layer helps to explain why a user is a hate speech spreader by producing attention weights at both token and post level. Our proposed model outperformed the state-of-the-art multilingual transformer models.
- Pub Date:
- September 2021
- Computer Science - Computation and Language;
- Computer Science - Artificial Intelligence;
- Computer Science - Machine Learning
- 9 pages, 2 figures, see the original paper: http://ceur-ws.org/Vol-2936/paper-157.pdf