BaitBuster-Bangla: A comprehensive dataset for clickbait detection in Bangla with multi-feature and multi-modal analysis

• Published in: Data in brief Vol. 53; p. 110239
• Main Authors: Imran, Abdullah Al, Shovon, Md Sakib Hossain, Mridha, M.F.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01-04-2024; Elsevier
• Subjects: Bangla clickbait dataset; Bangla natural language processing; Data; Multi-feature clickbait dataset; Multi-modal clickbait dataset; Social media analysis; User behavior modeling; YouTube clickbait; Bangla natural language processing; Multi-modal clickbait dataset; User behavior modeling; YouTube clickbait; Bangla clickbait dataset; Social media analysis; Multi-feature clickbait dataset
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2024.110239

This study presents a large multi-modal Bangla YouTube clickbait dataset consisting of 253,070 data points collected through an automated process using the YouTube API and Python web automation frameworks. The dataset contains 18 diverse features categorized into metadata, primary content, engagement statistics, and labels for individual videos from 58 Bangla YouTube channels. A rigorous preprocessing step has been applied to denoise, deduplicate, and remove bias from the features, ensuring unbiased and reliable analysis. As the largest and most robust clickbait corpus in Bangla to date, this dataset provides significant value for natural language processing and data science researchers seeking to advance modeling of clickbait phenomena in low-resource languages. Its multi-modal nature allows for comprehensive analyses of clickbait across content, user interactions, and linguistic dimensions to develop more sophisticated detection methods with cross-linguistic applications.