Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media

TL;DR

This paper presents a method for automatically detecting clickbaits in online news headlines and a browser extension that warns users and blocks such clickbaits, achieving high accuracy in detection and blocking.

Contribution

It introduces a novel clickbait detection system integrated into a browser extension that personalizes blocking based on user preferences.

Findings

93% accuracy in clickbait detection

89% accuracy in blocking clickbaits

Effective offline and online performance

Abstract

Most of the online news media outlets rely heavily on the revenues generated from the clicks made by their readers, and due to the presence of numerous such outlets, they need to compete with each other for reader attention. To attract the readers to click on an article and subsequently visit the media site, the outlets often come up with catchy headlines accompanying the article links, which lure the readers to click on the link. Such headlines are known as Clickbaits. While these baits may trick the readers into clicking, in the long run, clickbaits usually don't live up to the expectation of the readers, and leave them disappointed. In this work, we attempt to automatically detect clickbaits and then build a browser extension which warns the readers of different media sites about the possibility of being baited by such headlines. The extension also offers each reader an option to block clickbaits she doesn't want to see. Then, using such reader choices, the extension automatically blocks similar clickbaits during her future visits. We run extensive offline and online experiments across multiple media sites and find that the proposed clickbait detection and the personalized blocking approaches perform very well achieving 93% accuracy in detecting and 89% accuracy in blocking clickbaits.