Unified and Multilingual Author Profiling for Detecting Haters
This addresses the challenge of detecting online hate speech spreaders for social media platforms and moderators, though it appears incremental as it builds on existing transformer models with added attention for explanation.
The paper tackled the problem of identifying hate speech spreaders across multiple languages by developing a unified user profiling framework that processes tweets with sentence transformers and an attention mechanism, achieving state-of-the-art performance.
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.