CAREER: Towards Data-Driven Methods to Counter Online Aggression

社交媒体的兴起使得网络欺凌、骚扰和仇恨言论等网络攻击行为达到了前所未有的规模。一些攻击者选择他们的目标，并在两极分化的网络社区协调组织对受害者的攻击，用仇恨或令人不安的信息、视频和图像淹没他们。这些攻击会对受害者造成严重伤害，迫使他们离开社交媒体网站，甚至考虑自残。尽管网络攻击构成了威胁，但到目前为止，这个问题还没有得到计算机安全研究界的太多关注。然而，开发能够识别和减轻此类攻击的定量方法对于为所有互联网用户提供安全的在线体验至关重要。该项目旨在开发能够实时识别在线攻击的工具，使在线服务能够采取适当的对策。在这个项目中，PI旨在实现四个研究目标。首先，通过利用众包工作者的注释，该项目旨在为社交媒体上的攻击事件提供有效的确凿证据。去识别的数据集将公开发布，并将帮助整个研究界更好地理解这个问题。其次，PI将开发基于机器学习的技术，以识别参与在线攻击的在线帐户，并自动标记他们发布的仇恨内容，使在线服务能够快速应对此类攻击。第三，该项目将开发预测模型，以确定未来在线发布的内容受到仇恨的可能性；在线服务将能够使用这些模型主动地将审核资源分配给被认为有风险的内容。最后，PI将调查针对这个问题的不同缓解方法的优缺点，从暂停违规的在线账户到禁止对特别危险的内容发表评论。该项目的教育活动将包括一个针对非技术背景的大学生的跨学科模块，以及一个旨在让高中生更好地了解网络欺凌攻击的教程。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。

The rise of social media has enabled online aggression practices such as cyberbullying, harassment, and hate speech to reach an unprecedented scale. Some aggressors select their targets and coordinate on polarized online communities to organize attacks against their victims, inundating them with hateful or disturbing messages, videos, and images. These attacks can cause serious harm to their victims, forcing them to leave social media sites or even to contemplate self-harm. Despite the threat posed by online aggression, the problem has so far not received much attention by the computer security research community. Developing quantitative methods able to identify and mitigate such attacks is however of paramount importance to provide a safe online experience to all Internet users. This project aims to develop tools able to identify online aggression attacks in real time, allowing online services to take the appropriate countermeasures. In this project, the PI aims to achieve four research objectives. First, by leveraging annotation from crowdsourcing workers, this project aims to develop effective corroborating evidence of aggression incidents on social media. De-identified datasets will be released publicly and will help the research community at large to better understand the problem. Second, the PI will develop techniques based on machine learning to identify online accounts that partake in online aggression, and to automatically flag the hateful content that they post, allowing online services to quickly react to such attacks. Third, the project will develop predictive models to establish the likelihood for content that is posted online to receive hate in the future; online services will be able to use these models to proactively allocate moderation resources towards content that is considered at risk. Finally, the PI will investigate the advantages and disadvantages of different mitigation approaches for this problem, from suspending offending online accounts to disabling comments for particularly risky content. This project's educational activities will include an interdisciplinary module targeted at college students from non-technical backgrounds and a tutorial designed to provide high school students with a better understanding of cyberbullying attacks.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.