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

Facebook等社交媒体网站是传播clickbait的流行平台，clickbait是带有误导性标题的链接，无法兑现承诺。clickbait不仅浪费用户的时间，而且经常将用户引导到钓鱼网站和包含间谍软件和恶意软件的网站。大量用户成为社交媒体上诈骗的受害者，包括通过点击诱饵传播的诈骗，这是由于缺乏意识和社交媒体平台上缺乏适当的警告。这些用户很容易受到身份盗窃、在线黑客攻击以及将敏感信息暴露给对手的攻击。因此，限制点击诱饵对用户安全的影响至关重要。该项目正在开发新技术来检测各种形式的点击诱饵，特别是基于视频的点击诱饵，并研究社交媒体上的用户行为，以设计有效的警告系统。这项研究的结果正在被整合到一个名为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中的点击诱饵。该项目还旨在通过外联工作和制作视频，鼓励代表性不足的群体参与进来，以鼓励妇女将网络安全视为一项职业。检测点击诱饵是一项重大挑战，特别是随着视频成为一种更突出的在线媒体形式，破坏了检测误导性文本的努力。为了应对这一挑战，研究小组将采取综合方法，研究用于吸引用户点击的技术的效果，clickbait的呈现和分发，通过抓取用户的个人信息（即，有针对性的点击诱饵）、自动生成面部交换点击诱饵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点击诱饵数据集，以探索识别社交媒体上点击诱饵的方法。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，以自动识别视频点击诱饵。在该项目的另一个重点中，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点击诱饵的脆弱性，并研究用于吸引用户点击的各种社会工程技术的影响。这些发现正在被用于设计警告系统，该系统将被集成到BaitBuster 2.0中，以智能有效地警告用户避免点击诱饵。最后，警告系统和BaitBuster 2.0的可用性和有效性正在通过深入的用户研究进行评估。该奖项反映了NSF的法定使命，并被认为值得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。

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.