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.

社交媒体平台最近已开始应对删除或“替代”有害内容和恶意演员的压力。 该项目分析当前的替代策略是否减轻有害沟通。 该项目分析了消除仇恨言论和极端主义行为者和社区是否遏制恶意内容在社交媒体中的传播。该分析还可以揭示出列出的参与者是在边缘平台上重新组合，重新出现在潜在的数量中，发展更具凝聚力的社区，还是随着可恶的内容水平的增加而吸引更极端的追随者，也许是在不适应此内容的平台上。 通过这项研究，该项目对内容的审核和替代形式如何影响在线仇恨和极端主义的发展，从而设计了有效的政策，以限制在线恶意内容的设计。该项目正在收集有关在线仇恨群集或群体的数据，跨多个社交媒体平台。 项目团队正在构建一个机器学习工具，该工具可以使用这些数据来对广泛的仇恨语音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替代事件对仇恨言论的使用，音量和毒性的影响，以及在未修改平台上重组的小组在多大程度上。该项目的最终目标是建立一个动态的数学模型，以量化替代形式如何影响极端主义团体的出现及其随着时间的推移的发展。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来获得支持的。