  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.
