ArXiv

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

2026-07-19 · 10:42 UTC ·Md. Ariful Islam, Md Tanvirul Islam, Md. Maruf Hossain Miru... ·1 min read
Authors
Md. Ariful Islam, Md Tanvirul Islam, Md. Maruf Hossain Miru...
Categories
cs.CV
arXiv
https://arxiv.org/abs/2607.17182v1
PDF
https://arxiv.org/pdf/2607.17182v1

Brief

BanClickThumb introduces a 7,147-pair Bengali YouTube thumbnail-title dataset (five domains) annotated by ten annotators (Cohen's Kappa 0.83–0.93) to address limited multimodal resources for clickbait detection. Benchmarks show XLM-RoBERTa text model at 0.82, SwiftFormer image model at 0.68, and a ViT+XLM-RoBERTa fusion reaching 0.84 accuracy. Full text was not available; results come from the abstract.

Why it matters

BanClickThumb: a curated Bengali YouTube thumbnail-title dataset of 7,147 pairs across five content domains, annotated by ten annotators with high agreement (Cohen's Kappa 0.83–0.93).

Key details

  • Model benchmarks: text-only BanClickTextFormer (XLM-RoBERTa) achieves 0.82 accuracy, image-only BanClickImageFormer (SwiftFormer) 0.68, and multimodal BanClickFusionFormer (ViT + XLM-RoBERTa, intermediate fusion) 0.84 accuracy.
  • Error analysis identifies dense thumbnail text, figurative language, and culturally specific slang as remaining challenges; the dataset and benchmarks are released publicly to support low-resource multimodal research.
Source evidence

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 publicly available multimodal datasets are limited. To address this gap, we introduce BanClickThumb, a curated dataset of 7,147 Bengali YouTube thumbnail-title pairs from five content domains, annotated by ten annotators with high agreement (Cohen's Kappa: 0.83-0.93). Using this dataset, we benchmark text-only, image-only, and multimodal approaches. Among unimodal models, BanClickTextFormer (XLM-RoBERTa) achieves 0.82 accuracy, while BanClickImageFormer (SwiftFormer) reaches 0.68. Our proposed multimodal model, BanClickFusionFormer, combines ViT and XLM-RoBERTa through intermediate fusion and achieves the best accuracy of 0.84. Error analysis shows that dense thumbnail text, figurative language, and culturally specific slang remain challenging. Our findings demonstrate the effectiveness of multimodal fusion for Bengali clickbait detection and provide a publicly available benchmark to support future research on low-resource multimodal content analysis.