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New dataset and multimodal model tackle Bengali YouTube clickbait

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset and multimodal model tackle Bengali YouTube clickbait

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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]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Md. Ariful Islam, Md Tanvirul Islam, Md. Maruf Hossain Miru, Md Khalid Syfullah ·

    BanClickThumb: A Multimodal Dataset and Transformer Fusion Benchmarks for Clickbait Detection in Bengali YouTube Videos

    arXiv:2607.17182v1 Announce Type: new Abstract: Clickbait, where video titles and thumbnails exaggerate or misrepresent content, reduces user trust, wastes attention, and promotes misinformation on video-sharing platforms. Detecting Bengali clickbait remains challenging because p…

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