CAREER: Developing Computational Methods to predict Hate Crimes

职业：开发预测仇恨犯罪的计算方法

• 批准号: 1846531
• 负责人: Morteza Dehghani
• 金额: $ 71.13万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1846531&HistoricalAwards=false
• 关键词: CAREER; Developing; Computational; Methods; predict

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.

根深蒂固的群体信念会促使人们从事仇恨犯罪。然而，预测这些犯罪是极具挑战性的，因为他们的统计罕见。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。这项研究探讨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。科学的目标是告知和推进理论和基本理解的社会和认知过程的基础极端和仇恨的思想，以及他们如何传播。目的之一是确定可能抑制在社交媒体上传播仇恨言论的机制。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，并检查在线和社交媒体的思想传播。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，该项目旨在了解深层持有的信仰和身份动态如何结合，形成可能导致贬低外群体和仇恨犯罪的极端观点。 它研究了表征某些形式的社会认知和仇恨言论的语言特征，并考虑如何从稀疏，有偏见的数据估计仇恨言论的空间分布。 该研究考虑了是否可以使用不引人注目的措施和有关社交网络的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群体服务机构的学生提供辅导，与更广泛的科学界分享新开发的文本分析工具，以及关于机器学习和文本分析的讲习班。 这个职业奖是由NSF社会心理学和安全和值得信赖的网络空间（SaTC）计划共同支持的。这个奖项反映了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标准进行评估，被认为是值得支持的。

项目成果

Moral concerns are differentially observable in language

• DOI: 10.1016/j.cognition.2021.104696
• 发表时间: 2021-03-31
• 期刊: COGNITION
• 影响因子: 3.4
• 作者: Kennedy, Brendan;Atari, Mohammad;Dehghani, Morteza
• 通讯作者: Dehghani, Morteza

Measuring Abstract Mind-Sets Through Syntax: Automating the Linguistic Category Model

通过语法测量抽象思维模式：自动化语言类别模型

• DOI: 10.1177/1948550619848004
• 发表时间: 2020
• 期刊: Social Psychological and Personality Science
• 影响因子: 5.7
• 作者: Johnson-Grey, Kate M.;Boghrati, Reihane;Wakslak, Cheryl J.;Dehghani, Morteza
• 通讯作者: Dehghani, Morteza

Contextualizing Hate Speech Classifiers with Post-hoc Explanation

将仇恨言论分类器与事后解释结合起来

• DOI: 10.18653/v1/2020.acl-main.483
• 发表时间: 2020
• 期刊: Proceedings of the Annual Meeting of the Association for Computational Linguistics
• 作者: Kennedy, Brendan;Jin, Xisen;Mostafazadeh Davani, Aida;Dehghani, Morteza;Ren, Xiang
• 通讯作者: Ren, Xiang

Morally Homogeneous Networks and Radicalism

• DOI: 10.1177/19485506211059329
• 发表时间: 2021-12-16
• 期刊: SOCIAL PSYCHOLOGICAL AND PERSONALITY SCIENCE
• 影响因子: 5.7
• 作者: Atari, Mohammad;Davani, Aida Mostafazadeh;Dehghani, Morteza
• 通讯作者: Dehghani, Morteza

Moral values predict county-level COVID-19 vaccination rates in the United States.

道德价值观可预测美国县级 COVID-19 疫苗接种率。

• DOI: 10.1037/amp0001020
• 发表时间: 2022
• 期刊: American Psychologist
• 影响因子: 16.4
• 作者: Reimer, Nils Karl;Atari, Mohammad;Karimi-Malekabadi, Farzan;Trager, Jackson;Kennedy, Brendan;Graham, Jesse;Dehghani, Morteza
• 通讯作者: Dehghani, Morteza