Papers by Mohamed Bayan Kmainasi
CritiSense: Critical Digital Literacy and Resilience Against Misinformation (2026.acl-demo)
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Firoj Alam, Fatema Ahmad, Ali Ezzat Shahroor, Mohamed Bayan Kmainasi, Elisa Sartori, Giovanni Da San Martino, Abul Hasnat, Raian Ali
| Challenge: | a recent study found that social media misinformation is reactive and claim-specific, and can degrade under temporal and cross-lingual/domain shift. |
| Approach: | They present a mobile media-literacy app that builds digital literacy skills through short, interactive challenges with instant feedback. |
| Outcome: | The app is the first multilingual and modular platform to improve digital literacy skills. |
LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content (2025.findings-naacl)
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Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Maram Hasanain, Sahinur Rahman Laskar, Naeemul Hassan, Firoj Alam
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable success as general-purpose task solvers across various fields. |
| Approach: | They propose to develop a specialized LLM for analyzing news and social media content in a multilingual context. |
| Outcome: | The proposed model outperforms the current state-of-the-art on 23 testing sets and achieves comparable performance on 8 sets. |
MemeIntel: Explainable Detection of Propagandistic and Hateful Memes (2025.emnlp-main)
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| 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)
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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. |