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.

这项研究将为衡量在线社区中的激进化和去激进化奠定基础，方法是跟踪一段时间内的个人行为趋势和规模，并识别那些脱离了他们之前在激进化过程中加入的亚文化的成员的模式，但已经开始去激进化。网络极端主义和激进化已成为日益受到关注和活跃的研究领域。研究网络极端主义团体的跨学科工作非常广泛，但基于计算和数据科学的去激进化过程研究却很有限。主要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，其中包括与一种极端主义亚文化相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益于网上和线下的社会福祉，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典，群体归属感的信号，并构建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发现模式并进行内容分析以识别激进化标记。 （3）社会激励将研究社会互动及其与激进化的关系，以了解将社会激励和真实信仰联系起来的理论的适用性。 (4) 检测去激进化将识别退出论坛的成员以及为试图离开社区的成员寻求支持小组的成员中的去激进化标记。 （5）去激进化分类将建立机器学习模型，自动识别可能考虑去激进化的成员。本研究中开发的模型和词汇将为更广泛地理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，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。