Researchers have introduced Bn-HIB, a new dataset designed to detect hate and inflammatory content within Bengali internet memes. This dataset, containing 3,247 manually annotated memes, is notable for being the first to differentiate between inflammatory content and direct hate speech in Bengali. Alongside the dataset, a Multi-Modal Co-Attention Fusion Model (MCFM) was proposed, which analyzes both visual and textual elements of memes to improve classification accuracy. Experiments indicate that MCFM outperforms existing state-of-the-art models on the Bn-HIB dataset, and the dataset has been made publicly available. AI
IMPACT This research addresses a critical gap in low-resource language NLP, potentially improving content moderation and safety for Bengali-speaking online communities.
RANK_REASON The cluster describes a new academic paper introducing a novel dataset and model for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- Bangla
- Bangla Hate Inflammatory Benign
- Bn-HIB
- MCFM
- Mendeley Data
- Multi-Modal Co-Attention Fusion Model
- Rakib Ullah
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