Papers by Md. Rafiul Biswas
So Hateful! Building a Multi-Label Hate Speech Annotated Arabic Dataset (2024.lrec-main)
Copied to clipboard
| Challenge: | Social media enables widespread propagation of hate speech targeting groups based on ethnicity, religion, or other characteristics. |
| Approach: | They analyze 70,000 Arabic tweets to identify hate speech patterns and train models . 15% of tweets contain offensive language while 6% have hate speech . authors hope to prevent spread of hateful content on social media platforms . |
| Outcome: | The analysis of 70,000 Arabic tweets shows that 15% of tweets contain offensive language while 6% have hate speech . 10% of tweet provide verifiable factual claims, and 7% are deemed important . |
A Multi-Task Learning Framework for Modeling Engagement and Topic-Sensitive Responses in Arabic Women’s Discourse (2026.findings-eacl)
Copied to clipboard
| Challenge: | a corpus of 158k arab Facebook posts spanning women's rights, gender debates, and economic empowerment reveals patterns of public opinion that vary dramatically across regional and cultural contexts. |
| Approach: | They propose a multi-task learning framework that learns audience reaction classification and engagement magnitude regression and non-engagement detection. |
| Outcome: | The proposed model achieves a test macro-F1 of 72.4 and weighted-F1. It measures 158k posts across gender issues, legal rights advocacy, gender identity discussions, and economic empowerment. |