English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This study presents a large multi-modal Bangla YouTube clickbait datasetconsisting of 253,070 data points collected through an automated process usingthe YouTube API and Python web automation frameworks. The dataset contains 18diverse features categorized into metadata, primary content, engagementstatistics, and labels for individual videos from 58 Bangla YouTube channels. Arigorous preprocessing step has been applied to denoise, deduplicate, andremove bias from the features, ensuring unbiased and reliable analysis. As thelargest and most robust clickbait corpus in Bangla to date, this datasetprovides significant value for natural language processing and data scienceresearchers seeking to advance modeling of clickbait phenomena in low-resourcelanguages. Its multi-modal nature allows for comprehensive analyses ofclickbait across content, user interactions, and linguistic dimensions todevelop more sophisticated detection methods with cross-linguisticapplications.

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