GR-405 Boosting Clickbait Detection through Semantic Insights and Attention-Driven Neural Network

The digital age has witnessed an explosion of online content, making it increasingly challenging for users to differentiate between reliable information and clickbait, which is often misleading or sensationalized. Clickbait contributes to the spread of misinformation, phishing attacks, and illegal marketing practices, and manipulates users’ decisions. Even from a business standpoint a clickbait might not lead to a conversion, A user might land on the page by following a clickbait and get frustrated and close the page. Additionally, with the increase in the usage of large language models for content writing it is even more challenging for the general user to differentiate between clickbait and genuine content. As a result, clickbait detection has become an important research topic. Existing clickbait detection models often work on rule-based techniques which lack the nuanced understanding of human semantic knowledge, making them vulnerable to sophisticated clickbait techniques