Papers by Md Arid Hasan
POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization (2026.findings-acl)
Copied to clipboard
Usman Naseem, Robert Geislinger, Juan Ren, Sarah Kohail, Rudy Alexandro Garrido Veliz, P Sam Sahil, Yiran Zhang, Idris Abdulmumin, Marco Antonio Stranisci, Özge Alacam, Cengiz Acarturk, Aisha Jabr, Saba Anwar, Abinew Ali Ayele, Simona Frenda, Alessandra Teresa Cignarella, Elena Tutubalina, Oleg Rogov, Aung Kyaw Htet, Xintong Wang, Surendrabikram Thapa, Kritesh Rauniyar, Tanmoy Chakraborty, MD Arfeen Zeeshan, Dheeraj Kodati, Satya Keerthi, Sahar Moradizeyveh, Firoj Alam, Md Arid Hasan, Syed Ishtiaque Ahmed, Ye Kyaw Thu, Shantipriya Parida, Ihsan Ayyub Qazi, Lilian Diana Awuor Wanzare, Nelson Odhiambo Onyango, Clemencia Siro, Jane Wanjiru Kimani, Ibrahim Said Ahmad, Adem Chanie Ali, Martin Semmann, Chris Biemann, Shamsuddeen Hassan Muhammad, Seid Muhie Yimam
| Challenge: | polarization is a pervasive threat to democratic institutions, civil discourse, and social cohesion worldwide . most existing datasets focus on English or high-resource languages, reflecting a widespread trend across NLP tasks . |
| Approach: | They propose a multilingual, multicultural, and multi-event dataset with over 110K instances in 22 languages drawn from diverse online platforms and real-world events. |
| Outcome: | The proposed dataset analyzes polarization detection, type, and manifestation using a variety of annotation platforms adapted to each cultural context. |
MemeIntel: Explainable Detection of Propagandistic and Hateful Memes (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for label detection and explanation generation have been limited in understanding complex issues . identifying propaganda and hate in memes is essential for combating misinformation and minimizing harm . |
| Approach: | They propose an explanation-enhanced dataset for propaganda memes in Arabic and hateful memes on English to solve these tasks. |
| Outcome: | The proposed model outperforms the current state-of-the-art in label detection and explanation generation. |
PropXplain: Can LLMs Enable Explainable Propaganda Detection? (2025.findings-emnlp)
Copied to clipboard
Maram Hasanain, Md Arid Hasan, Mohamed Bayan Kmainasi, Elisa Sartori, Ali Ezzat Shahroor, Giovanni Da San Martino, Firoj Alam
| Challenge: | Currently, propagandistic content detection studies focus on detection, with little attention given to explanations justifying the predicted label. |
| Approach: | They propose a multilingual explanation-enhanced dataset and an explanation-based LLM to address this issue. |
| Outcome: | The proposed model performs comparably while also generating explanations. |
LLM-Based Multi-Task Bangla Hate Speech Detection: Type, Severity, and Target (2026.acl-long)
Copied to clipboard
| Challenge: | Existing work on social media platforms is limited in its ability to detect hate speech . a lack of reliable and scalable automated hate speech detection systems is a challenge for low-resource languages like Bangla. |
| Approach: | They propose to use a single-task, single-targeted, single language dataset to identify hate speech in Bangla. |
| Outcome: | The proposed dataset is the largest manually annotated Bangla hate-speech dataset to date. |