New dataset and multimodal model tackle Bengali YouTube clickbait

Researchers have introduced BanClickThumb, a new multimodal dataset designed to detect clickbait in Bengali YouTube videos. The dataset comprises 7,147 thumbnail-title pairs and was used to benchmark various detection models. A proposed multimodal model, BanClickFusionFormer, which combines visual and textual transformers, achieved the highest accuracy of 0.84, outperforming unimodal approaches. AI

IMPACT This research provides a new benchmark and model for detecting clickbait in low-resource languages, potentially improving user experience on video platforms.

RANK_REASON The cluster contains a research paper introducing a new dataset and benchmark for clickbait detection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• BanClickFusionFormer
• BanClickImageFormer
• BanClickTextFormer
• BanClickThumb
• Bengali
• Md. Ariful Islam
• SwiftFormer
• ViT
• XLM-RoBERTa
• YouTube

• paper
• product

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