Article
Clickbait Detection in YouTube Videos Using Thumbnail and Content Analysis
Clickbait represents a deceptive content strategy whereby exaggerated titles and visually manipulative thumbnails are employed to attract user attention without delivering proportionate content quality. This practice gradually diminishes user trust and undermines platform credibility. The present work proposes an automated multimodal clickbait detection framework for YouTube videos that analyzes video titles, thumbnail text extracted via Optical Character Recognition, transcripts, and engagement metrics to classify content as Clickbait or Non-clickbait with associated confidence scores. The system employs Term Frequency-Inverse Document Frequency based text feature extraction combined with supervised learning classifiers including Support Vector Machines and Logistic Regression. Experimental evaluation demonstrates that the proposed approach achieves classification accuracy exceeding 88 percent on standard datasets while maintaining computational efficiency suitable for near real-time deployment. The implementation as a lightweight Flask web application enables practical accessibility for end users seeking to evaluate video credibility before consumption
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