Social media sites like Facebook are popular platforms for spreading clickbait, links with misleading titles that do not deliver on their promises. Not only does clickbait waste users’ time, it often directs users to phishing sites and sites containing spyware and malware. A large number of users fall victim to scams on social media, including those spread through clickbait, due to both a lack of awareness and a lack of appropriate warnings on social media platforms. As a result, users are vulnerable to identity theft, online hacking, and the exposure of sensitive information to adversaries. Thus, it is critical to limit the impact of clickbait on users' security. To achieve this goal, this project is developing novel techniques to detect various forms of clickbait, especially video-based clickbait, and study user behavior on social media to design effective warning systems. The findings from this research are being incorporated into an open-source browser extension called Baitbuster 2.0, building on the original Baitbuster tool for detecting text-based clickbait. To enhance the impact of this tool, the researchers will design new training methods to raise security awareness and help users avoid clickbait in social media. The project also aims to engage underrepresented groups via outreach efforts and through developing videos to encourage girls to consider cybersecurity as a career.

Related Publications


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