HCC: Small: Assessing Radicalization and Deradicalization Online

HCC：小型：在线评估激进化和去激进化

• 批准号: 2331257
• 负责人: Jennifer Golbeck
• 金额: $ 58.36万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331257&HistoricalAwards=false
• 关键词: HCC; Small; Assessing; Radicalization; Deradicalization

This research will develop a foundation for measuring radicalization and, importantly, deradicalization, in online communities by tracking individual behavioral trends over time and at scale, and identifying patterns among members who disengage from the subculture they had earlier joined in a process of radicalization, but had begun to deradicalize. Online extremism and radicalization has been a growing concern and active area of research. There is a broad body of cross-disciplinary work studying online extremist groups but limited computational and data science-based research on the deradicalization process. The primary empirical research will analyze a recently-collected large corpus of over 8 million online forum posts, including comprehensive archives of five popular forums related to one extremist subculture. The practical implications of this work have potential to benefit the well-being of society, both online and offline, and to bolster national security.This work includes five core projects: (1) Detecting Radicalization will build a formal lexicon of subcultural language, a signal of belonging in the group, and build a suite of tools to detect hate speech among members. (2) Detecting Radicalization will analyze trends of language use per user to find patterns and conduct content analysis to identify markers of radicalization. (3) Social Incentives will study social interaction and its relationship with radicalization to understand the applicability of theories that connect social incentives and genuine beliefs. (4) Detecting Deradicalization will identify markers of deradicalization among members who disengage from forums and those who turn to support groups for members who are trying to leave the community. (5) Classifying Deradicalization will build machine-learning models to automatically identify members who may be considering deradicalization. The models and lexicons developed in this research will provide methods for understanding the social and normative dynamics of extremist groups and subcultures more broadly.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.

这项研究将建立一个基础，以衡量激进化，重要的是，去激进化，在网上社区跟踪个人的行为趋势，随着时间的推移和规模，并确定模式的成员谁脱离亚文化，他们早些时候加入了激进化的过程中，但已经开始去激进化。网络极端主义和激进化已成为一个日益令人关切和活跃的研究领域。有一个广泛的跨学科的工作研究在线极端主义团体，但有限的计算和数据科学为基础的研究去激进化的过程。主要的实证研究将分析最近收集的超过800万个在线论坛帖子的大型语料库，包括与一种极端主义亚文化有关的五个流行论坛的综合档案。这项工作的实际意义有可能有利于社会的福祉，无论是在线还是离线，并加强国家安全。这项工作包括五个核心项目：（1）检测激进化将建立一个正式的亚文化语言词汇，属于该群体的信号，并建立一套工具来检测成员之间的仇恨言论。(2)检测激进化将分析每个用户的语言使用趋势，以找到模式并进行内容分析，以确定激进化的标记。(3)社会激励将研究社会互动及其与激进化的关系，以了解连接社会激励和真正信仰的理论的适用性。(4)检测去激进化将在脱离论坛的成员和那些转向支持试图离开社区的成员的团体中确定去激进化的标志。(5)分类去激进化将建立机器学习模型，以自动识别可能考虑去激进化的成员。该研究中开发的模型和词汇将为更广泛地了解极端主义团体和亚文化的社会和规范动态提供方法。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来支持。