Clickbait Detection

Clickbait detection research aims to automatically identify online content designed to deceptively lure users through sensationalized headlines. Current approaches leverage various techniques, including transformer-based models, semi-supervised learning with GANs, and contrastive learning methods that compare headline and content similarity, often incorporating text summarization to bridge length discrepancies. This field is crucial for mitigating the negative impacts of clickbait on user experience and information quality, with ongoing efforts focused on developing robust models for diverse languages and content types, including videos and news articles.