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.

此研究提供了一个包含 18 个多模式孟加拉语 YouTube 标题党数据集，其中包括来自 58 个孟加拉语 YouTube 频道的 253,070 个数据点。这是迄今为止最大且最强大的孟加拉语标题党语料库，可用于自然语言处理和数据科学研究，以推进低资源语言中标题党现象的建模。它的多模式特性允许通过内容、用户互动和语言维度进行全面分析，以开发具有跨语言应用的更复杂的检测方法。