Social media sites such as 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. These 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. 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 women to consider cybersecurity as a career.
Detecting clickbait is a major challenge, particularly as video becomes a more prominent form of media online, undermining efforts to detect misleading text. To address this challenge, the research team will take an integrated approach examining the effects of techniques used to attract clicks from users, presentation and distribution of clickbait, personalization of clickbait through crawling users? personal information (i.e., targeted clickbait), automatic generation of face-swapping clickbait, and risk perceptions and security awareness of users. As a first step, the researchers are collecting and analyzing clickbait datasets to explore ways of identifying clickbait on social media. Using these datasets, they are developing novel applications of state-of-the-art machine learning techniques such as optical character recognition and video understanding to automatically identify video clickbait. In another thrust of this project, the researchers are studying users' clicking behavior and corresponding security mental models to better understand their vulnerability to clickbait and examine the effects of a wide range of social engineering techniques used to attract clicks from users. The findings are being used to design warning systems, which will be integrated into BaitBuster 2.0, to warn users intelligently and effectively to avoid clickbait. Finally, the usability and efficacy of the warning system and BaitBuster 2.0 are being evaluated through in-depth user studies.