SaTC: CORE: Medium: Collaborative: BaitBuster 2.0: Keeping Users Away From Clickbait

Facebook等社交媒体网站是传播标题党（clickbait）的热门平台，这些链接带有误导性标题，但并没有兑现承诺。标题党不仅浪费用户的时间，还经常将用户引导到网络钓鱼网站和包含间谍软件和恶意软件的网站。由于社交媒体平台上缺乏意识和适当的警告，大量用户成为社交媒体上的骗局的受害者，包括通过标题党传播的骗局。这些用户很容易受到身份盗窃、在线黑客攻击和敏感信息暴露给对手的攻击。因此，限制标题党对用户安全的影响是至关重要的。该项目正在开发新技术来检测各种形式的标题党，特别是基于视频的标题党，并研究社交媒体上的用户行为，以设计有效的预警系统。这项研究的结果被整合到一个名为Baitbuster 2.0的开源浏览器扩展中，该扩展基于最初的Baitbuster工具，用于检测基于文本的点击诱饵。为了增强该工具的影响力，研究人员将设计新的培训方法，以提高安全意识，并帮助用户避免社交媒体上的标题党。该项目还旨在通过外展努力和制作视频来吸引代表性不足的群体，鼓励女性将网络安全视为一种职业。检测标题党是一项重大挑战，尤其是随着视频成为一种更突出的在线媒体形式，这破坏了检测误导性文本的努力。为了应对这一挑战，研究团队将采用综合的方法来检查用于吸引用户点击的技术的影响，标题党的呈现和分发，通过抓取用户个人信息的个性化标题党（即有针对性的标题党），自动生成换脸标题党，以及用户的风险感知和安全意识。作为第一步，研究人员正在收集和分析标题党数据集，以探索在社交媒体上识别标题党的方法。利用这些数据集，他们正在开发最先进的机器学习技术的新应用，如光学字符识别和视频理解，以自动识别视频标题党。该项目的另一个重点是，研究人员正在研究用户的点击行为和相应的安全心理模型，以更好地了解他们对标题党的脆弱性，并检查用于吸引用户点击的各种社会工程技术的影响。这些发现正被用于设计预警系统，并将集成到BaitBuster 2.0中，以智能有效地警告用户避免点击诱饵。最后，通过深入的用户研究，评估预警系统和BaitBuster 2.0的可用性和有效性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.