BaitBuster-Bangla: A Comprehensive Dataset for Clickbait Detection in Bangla with Multi-Feature and Multi-Modal Analysis

Description

This dataset is a multi-feature and multi-modal dataset for Bangla clickbait detection in video sharing platforms. The dataset is collected from YouTube using its official public API with the objective of classifying clickbait content in the Bangla language. The dataset consists of 253,070 entries with 18 columns covering a curated list of 28 Not Clickbait, and 26 Clickbait Bangla youtube channels. The dataset provides valuable information for studying clickbait content and includes various metadata related to the videos, user engagement statistics, and labels. The dataset has been labeled in three different strategies: i) pre-defined auto labels, ii) labels by human annotator, and iii) labels by fine-tuned AI model. However, human labels are are available for 10000 entries. The dataset is available in three different formats: xlsx, csv, and parquet.

Data Dictionary

Feature Type	Feature Name	Data Type	Definition
Metadata	channel_id	string	ID of the YouTube channel
Metadata	channel_name	string	Name of the YouTube channel
Metadata	channel_url	string	URL of the YouTube channel
Metadata	video_id	string	ID of the video
Metadata	publishedAt	datetime	Date and time when the video was published
Primary Data	title	string	Title of the video
Primary Data (Processed)	title_debiased	string	Debiased title of the video
Primary Data	description	string	Debiased description of the video
Primary Data (Processed)	description_debiased	string	Description of the YouTube video without bias
Metadata	url	string	URL of the video
Engagement Stats	viewCount	int	Number of views the video has received
Engagement Stats	commentCount	int	Number of comments on the video
Engagement Stats	likeCount	int	Number of likes on the video
Engagement Stats	dislikeCount	int	Number of dislikes on the video
Metadata	thumbnails	string	URL of the thumbnail for the video
Label	auto_labeled	string	Automatically labeled using manual review
Label (Processed)	human_labeled	string	Labeled by human
Label (Processed)	ai_labeled	string	Labeled by an AI model fine-tuned on human labeled data

Paper

• Data in Brief: https://doi.org/10.1016/j.dib.2024.110239
• arXiv Link: https://arxiv.org/abs/2310.11465

Dataset

• Mendeley: https://data.mendeley.com/datasets/3c6ztw5nft/
• Kaggle: https://www.kaggle.com/datasets/abdalimran/baitbuster-bangla
• Huggingface: https://huggingface.co/datasets/abdalimran/BaitBuster-Bangla

Citation

MLA

Al Imran, Abdullah, Md Sakib Hossain Shovon, and M. F. Mridha. "BaitBuster-Bangla: A Comprehensive Dataset for Clickbait Detection in Bangla with Multi-Feature and Multi-Modal Analysis." Data in Brief (2024): 110239.

BibText

@article{IMRAN2024110239,
title = {BaitBuster-Bangla: A Comprehensive Dataset for Clickbait Detection in Bangla with Multi-Feature and Multi-Modal Analysis},
journal = {Data in Brief},
pages = {110239},
year = {2024},
issn = {2352-3409},
doi = {https://doi.org/10.1016/j.dib.2024.110239},
url = {https://www.sciencedirect.com/science/article/pii/S2352340924002105},
author = {Abdullah Al Imran and Md Sakib Hossain Shovon and M.F. Mridha},
keywords = {Bangla clickbait dataset, YouTube clickbait, Multi-modal clickbait dataset, Multi-feature clickbait dataset, Bangla natural language processing, User behavior modeling, Social Media Analysis},
abstract = {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.}
}