Decoding Clickbait

Clickbait headlines undermine trust in digital journalism by exploiting readers' emotions rather than delivering substantive information. Prior research has largely focused on coarse sentiment polarity, leaving the role of fine-grained emotions underexplored. This study proposes a multi-stage artificial intelligence framework that incorporates RoBERTa-based emotion classification, Spearman correlation analysis, topic modeling, and association rule mining to analyze the clickbait data. Findings from the framework reveal that surprise is the strongest positive correlation of clickbait severity, while sadness, anger, and fear are negatively associated. Also, high-severity clickbait relies on isolated surprise, whereas legitimate news often combines surprise with a neutral tone. These results define an emotional fingerprint of clickbait, offering new theoretical insights into emotional persuasion and practical guidance for news recommendation systems and content governance on social platforms.