EAGER: DCL: SaTC: Enabling Interdisciplinary Collaboration: Deplatforming and Online Hate Speech Across the Social Media Ecology

EAGER：DCL：SaTC：实现跨学科合作：社交媒体生态中的去平台化和在线仇恨言论

• 批准号: 2210023
• 负责人: Yonatan Lupu
• 金额: $ 29.98万
• 依托单位: George Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210023&HistoricalAwards=false
• 关键词: EAGER; DCL; SaTC; Enabling; Interdisciplinary

Social media platforms have recently begun to respond to pressure to remove or "deplatform" harmful content and malicious actors. This project analyzes whether current deplatforming strategies mitigate harmful communication. The project analyzes whether removing hate speech and extremist actors and communities curbs the spread of malicious content throughout social media. The analysis also can reveal whether whether deplatformed actors regroup on fringe platforms, reappear in potentially larger numbers, grow more cohesive communities, or attract a more extreme following with increased levels of hateful content, perhaps on platforms that do not moderate this content. Through this research, the project provides a new understanding of how content moderation and deplatforming affect the development of online hate and extremism, leading to the design of effective policies to limit malicious content online.This project is collecting data on online hate clusters, or groups, across multiple social media platforms. The project team is constructing a machine-learning tool that can use these data to classify a wide range of hate speech types and targets, and the extent to which social media posts contain these types and targets. The team uses statistical models to analyze the effects of deplatforming events on the use, volume, and toxicity of hate speech, as well as on the extent to which groups reorganize on unmoderated platforms. The ultimate goal of the project is to build a dynamic, mathematical model to quantify how deplatforming affects the emergence of extremist groups and their evolution over time.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.

社交媒体平台最近开始应对删除或“去平台化”有害内容和恶意行为者的压力。 该项目分析了当前的去平台化策略是否可以减少有害沟通。 该项目分析消除仇恨言论、极端主义行为者和社区是否可以遏制恶意内容在社交媒体上的传播。该分析还可以揭示去平台行为者是否会在边缘平台上重新集结，是否会以更大的数量重新出现，发展更有凝聚力的社区，或者通过增加仇恨内容来吸引更极端的追随者（也许是在不调节这些内容的平台上）。 通过这项研究，该项目对内容审核和去平台化如何影响在线仇恨和极端主义的发展提供了新的认识，从而设计出有效的政策来限制在线恶意内容。该项目正在收集有关在线仇恨集群或团体的数据，跨多个社交媒体平台。 该项目团队正在构建一种机器学习工具，可以使用这些数据对各种仇恨言论类型和目标进行分类，以及社交媒体帖子包含这些类型和目标的程度。该团队使用统计模型来分析平台化事件对仇恨言论的使用、数量和毒性的影响，以及群体在不受监管的平台上重组的程度。该项目的最终目标是建立一个动态的数学模型，以量化去平台化如何影响极端主义团体的出现及其随时间的演变。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持以及更广泛的影响审查标准。