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Most of the online news media outlets rely heavily on the revenues generatedfrom the clicks made by their readers, and due to the presence of numerous suchoutlets, they need to compete with each other for reader attention. To attractthe readers to click on an article and subsequently visit the media site, theoutlets often come up with catchy headlines accompanying the article links,which lure the readers to click on the link. Such headlines are known asClickbaits. While these baits may trick the readers into clicking, in the longrun, clickbaits usually don't live up to the expectation of the readers, andleave them disappointed.  In this work, we attempt to automatically detect clickbaits and then build abrowser extension which warns the readers of different media sites about thepossibility of being baited by such headlines. The extension also offers eachreader an option to block clickbaits she doesn't want to see. Then, using suchreader choices, the extension automatically blocks similar clickbaits duringher future visits. We run extensive offline and online experiments acrossmultiple media sites and find that the proposed clickbait detection and thepersonalized blocking approaches perform very well achieving 93% accuracy indetecting and 89% accuracy in blocking clickbaits.

Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media