CAREER: Developing Computational Methods to predict Hate Crimes

根深蒂固的群体信仰可以激发人们从事仇恨犯罪。然而，由于统计上的罕见性，预测这些犯罪极具挑战性。该项目使用计算方法和来自社交媒体的大数据来预测恶毒的仇恨言论何时转变为仇恨犯罪。该研究考察了仇恨言论的语言及其在社交网络上的传播如何预测仇恨犯罪。科学目标是告知和推进关于极端和仇恨思想及其传播方式背后的社会和认知过程的理论和基本理解。其中一个目标是确定可能抑制社交媒体上仇恨言论传播的机制。另一个是为执法提供新的工具。许多学科的学生和教师将有机会学习处理大数据的新计算方法，以及在线和通过社交媒体检查思想传播的方法。通过将社会认知理论与自然语言处理和机器学习技术相结合，该项目旨在了解根深蒂固的信仰和身份动态如何结合起来形成极端的观点，从而导致对外群体的贬低和仇恨犯罪。它考察了表征某些形式的社会认知和仇恨言论的语言特征，并考虑了如何从稀疏的、有偏见的数据中估计仇恨言论的空间分布。该研究考虑了是否可以使用不显眼的措施和社交网络信息来预测出于社会动机的仇恨犯罪和仇恨言论行为。相关的教育和培训机会包括为少数族裔服务机构的学生提供指导，与更广泛的科学界分享最新开发的文本分析工具，以及关于机器学习和文本分析的研讨会。该职业奖由美国国家科学基金会社会心理学和安全与可信网络空间（SaTC）项目联合支持。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

Deeply-held group beliefs can motivate people to engage in hate crimes. Yet predicting these crimes is extremely challenging because of their statistical rarity. This project uses computational methods and big data from social media to predict when virulent hate speech transitions to hate crimes. The research examines how the language of hateful speech and its dissemination over social networks predicts hate crimes. The scientific goal is to inform and advance theory and basic understanding of the social and cognitive processes that underlie extreme and hateful thoughts and how they spread. One aim is to identify mechanisms that might inhibit the dissemination of hate speech in social media. Another is to provide new tools for law enforcement. Students and faculty across many disciplines will have an opportunity to learn new computational methods for handling big data and for examining the spread of ideas online and through social media. By integrating theories of social cognition with natural language processing and machine learning techniques, this project seeks to understand how deeply-held beliefs and identity dynamics coalesce to form extreme perspectives that could result in derogation of out-groups and hate crimes. It examines the linguistic features that characterize certain forms of social cognition and hate speech, and considers how the spatial distribution of hate speech can be estimated from sparse, biased data. The research considers whether socially-motivated hate crimes and acts of hate speech can be predicted using unobtrusive measures and information about social networks. The related educational and training opportunities include mentoring of students from minority-serving institutions, sharing newly-developed tools for text analysis with the wider scientific community, and workshops on machine learning and text analysis. This CAREER award is supported jointly by the NSF Social Psychology and Secure and Trustworthy Cyberspace (SaTC) Programs.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.