B V, ShashankShashankB VS, SanjaySanjaySK, UpendraUpendraKS, RakshithRakshithSK, Ramalakshmi2026-03-232026-03-232021-06https://gnanaganga.alliance.edu.in/handle/123456789/10004Online news media sometimes use misleading headlines to lure users into opening the news articles. These catchy headlines that attract users but disappoint at the end, are called clickbaits. Most of the time, they look far more interesting than the real article in order to entice clicks from the readers or motivate them to subscribe. Online news media rely on revenue generated by users clicking on their articles. Due to the importance of automatic clickbait detection on social media, many machine learning methods have been proposed and employed to find clickbait headlines. In machine learning and other related fields, there have been extensive studies on identifying bad quality content on the web such as spam and fake web pages. However, clickbaits are not necessarily spam or fake pages, but they can be genuine pages delivering low-quality content with exaggerated titles. We propose to use neural network to detect clickbaity links and warn the user to avoid those pages.enClickbaittext::report