Clicksense: AI – Based System for Misleading Thumbnail Detection
DOI:
https://doi.org/10.15662/IJEETR.2026.0802377Keywords:
Clickbait Thumbnails, Misleading Visuals, Sentiment Analysis, VADER, BERT, Natural Language Processing(NLP), Video Recommendation System.Abstract
Clickbait thumbnails are images that attract viewers by showing misleading visuals different from the actual video content. This often causes disappointment and reduces trust in video platforms. To solve this problem, our project uses user comments to detect misleading thumbnails automatically. We apply VADER sentiment analysis to find negative or critical comments and use BERT to classify whether comments relate to clickbait or not. Based on this, the system calculates a Clickbait Score that shows how likely a thumbnail is to be deceptive. It also includes a recommendation module that alerts users about possible clickbait videos. By analyzing user feedback instead of just images, our system, called ClickSense, offers a faster and more accurate way to identify misleading thumbnails and improve user experience on video-sharing platforms
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