The Clickbait Challenge 2017: Towards a Regression Model for Clickbait Strength

Clickbait has grown to become a nuisance to social media users and social media operators alike. Malicious content publishers misuse social media to manipulate as many users as possible to visit their websites using clickbait messages. Machine learning technology may help to handle this problem, giving rise to automatic clickbait detection. To accelerate progress in this direction, we organized the Clickbait Challenge 2017, a shared task inviting the submission of clickbait detectors for a comparative evaluation. A total of 13 detectors have been submitted, achieving significant improvements over the previous state of the art in terms of detection performance. Also, many of the submitted approaches have been published open source, rendering them reproducible, and a good starting point for newcomers. While the 2017 challenge has passed, we maintain the evaluation system and answer to new registrations in support of the ongoing research on better clickbait detectors

Is it a click bait? Let's predict using Machine Learning

In this era of digitisation, news reader tend to read news online. This is because, online media instantly provides access to a wide variety of content. Thus, people don't have to wait for tomorrow's newspaper to know what's happening today. Along with these virtues, online news have some vices as well. One such vice is presence of social media posts (tweets) relating to news articles whose sole purpose is to draw attention of the users rather than directing them to read the actual content. Such posts are referred to as clickbaits. The objective of this project is to develop a system which would be capable of predicting how likely are the social media posts (tweets) relating to new articles tend to be clickbait.Comment: M.Tech Thesis defended at BITS, Pilan