CAREER: Towards Data-Driven Methods to Counter Online Aggression

职业：寻找数据驱动的方法来对抗网络攻击

• 批准号: 1942610
• 负责人: Gianluca Stringhini
• 金额: $ 54.93万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942610&HistoricalAwards=false
• 关键词: CAREER; Towards; Data; Driven; Methods

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.

社交媒体的兴起使网络侵略实践（例如网络欺凌，骚扰和仇恨言论）达到了前所未有的规模。一些侵略者选择目标，并协调两极分化的在线社区，以组织对受害者的攻击，以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，甚至考虑自我伤害。尽管在线侵略构成的威胁，但到目前为止，该问题还没有得到计算机安全研究界的关注。但是，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侵略攻击的工具，从而使在线服务可以采用适当的对策。 在该项目中，PI旨在实现四个研究目标。首先，通过利用众包工人的注释，该项目旨在在社交媒体上开发有效的攻击事件证据。取消识别的数据集将公开发布，并将帮助整个研究社区更好地理解该问题。其次，PI将基于机器学习开发技术，以识别参与在线侵略的在线帐户，并自动标记其发布的可恶内容，从而使在线服务可以快速对此类攻击做出反应。第三，该项目将开发预测模型，以建立在线发布以将来收到仇恨的内容的可能性；在线服务将能够使用这些模型将适度资源分配给被认为处于危险的内容。最后，PI将研究此问题的不同缓解方法的优势和缺点，从暂停有罪的在线帐户到禁用特别风险的内容的评论。该项目的教育活动将包括针对来自非技术背景的大学生的跨学科模块，旨在为高中生提供更好地了解网络欺凌攻击的教程。该奖项反映了NSF的法定任务，并被认为是通过该基金会的知识分子功能和广泛影响的评估来评估Criteria criteria criteria criteria criperia creteria criperia criteria。

项目成果

"I'm a Professor, which isn't usually a dangerous job": Internet-facilitated Harassment and Its Impact on Researchers

“我是一名教授，这通常不是一项危险的工作”：互联网引发的骚扰及其对研究人员的影响

• DOI: 10.1145/3476082
• 发表时间: 2021
• 期刊: Proceedings of the ACM on Human-Computer Interaction
• 作者: Doerfler, Periwinkle;Forte, Andrea;De Cristofaro, Emiliano;Stringhini, Gianluca;Blackburn, Jeremy;McCoy, Damon
• 通讯作者: McCoy, Damon

Non-Polar Opposites: Analyzing the Relationship Between Echo Chambers and Hostile Intergroup Interactions on Reddit

非极性对立：分析回声室与 Reddit 上敌对群体间互动之间的关系

• 发表时间: 2023
• 期刊: Proceedings of the AAAI International Conference on Web and Social Media
• 作者: Efstratiou, A.;Blackburn, J.;Caulfield, T.;Stringhini, G.;Zannettou, S.;De Cristofaro, E.
• 通讯作者: De Cristofaro, E.

Proceedings of the AAAI International Conference on Web and Social Media (ICWSM)

AAAI 国际网络和社交媒体会议 (ICWSM) 会议记录

• 发表时间: 2021
• 期刊: Proceedings of the AAAI International Conference on Web and Social Media (ICWSM
• 作者: Wang, Yuping;Tahmasbi, Fatemeh;Blackburn, Jeremy;Bradlyn, Barry;De Cristofaro, Emiliano;Magerman, David;Zannettou, Savvas;Stringhini, Gianluca
• 通讯作者: Stringhini, Gianluca

Lambretta: Learning to Rank for Twitter Soft Moderation

• DOI: 10.1109/sp46215.2023.10179392
• 发表时间: 2022-12
• 期刊: 2023 IEEE Symposium on Security and Privacy (SP)
• 作者: Pujan Paudel;Jeremy Blackburn;Emiliano De Cristofaro;Savvas Zannettou;G. Stringhini

TrollMagnifier: Detecting State-Sponsored Troll Accounts on Reddit

• DOI: 10.1109/sp46214.2022.9833706
• 发表时间: 2021-12
• 期刊: 2022 IEEE Symposium on Security and Privacy (SP)
• 作者: Mohammad Hammas Saeed;Shiza Ali;Jeremy Blackburn;Emiliano De Cristofaro;Savvas Zannettou;G. Stringhini