Clickbait Detection

The term clickbait refers to a form of web content that employs writing formulas and linguistic techniques in headlines to trick readers into clicking links, but does not deliver on its promises. Although media scholars and pundits consistently show clickbait content in a bad light, the industry based on this type of content has been rapidly growing and reaching more and more people across the world. The growth of clickbait industry appears to have noticeable impact on the media ecosystem, as many traditional media organizations have started to use clickbait techniques to attract readers and generate revenue. The key purpose of this project is to systematically quantify the extents to which traditional print and broadcast media as well as the "unreliable" media use clickbait properties in contents published on the web. We also aim to design and develop clickbait detection algorithms that can identify misleading clickbaits and thereby improve social media using experience.

Related Publications


Yoo Yeon Sung, Jordan Boyd-Graber, Naeemul Hassan. Not all fake news is written: a dataset and analysis of misleading video headlines. In Proceedings of the 2023 Empirical Methods in Natural Language Processing. 2023


Maria D. Molina, S. Shyam Sundar, Md Main Uddin Rony, Naeemul Hassan, Dongwon Lee, Thai Le. Does clickbait actually attract more clicks? three clickbait studies you must read.. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 2021


Rony, Md Main Uddin; Hassan, Naeemul; Yousuf, Mohammad; . Baitbuster: a clickbait identification framework. Thirty-Second AAAI Conference on Artificial Intelligence. 2018


Rony, Md Main Uddin; Hassan, Naeemul; Yousuf, Mohammad; . Diving deep into clickbaits: who use them to what extents in which topics with what effects?. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. 2017