Predicting the Spread and Damage of Hate Speech for Effective Prevention and Intervention of Cyberhate

预测仇恨言论的传播和危害，有效预防和干预网络仇恨

• 批准号: 2691176
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2691176
• 关键词: Predicting; Spread; Damage; Hate; Speech

The project will develop novel Natural Language Processing (NLP) methods and metric for the automated prediction of the spread and damage of online hate speech. Despite significant research into studying online hate speech in the last decade, a critical limitation of existing work is that the focus has been to address 'where hate is', rather than 'where hate will be'. Predicting where hate speech will emerge, how it will spread, and to what extent it may damage online communities is crucial for the effective use of limited resources for prevention and intervention, where social media companies and NGOs invest hundreds of millions every year and yet still struggle to mitigate cyberhate. Therefore, this project will make an invaluable step towards addressing this gap in research, and develop more effective approaches and tools to address the problem.The project will be based around the combination of computational and social sciences research methods. Although both areas have studied the problem, there has been little crossover despite the benefits a combined approach would bring. This project will: 1) create new datasets addressing online hate speech prediction tasks; 2) develop metrics to evaluate the damage of online hate speech and its damage to online communities; and 3) develop new NLP methods to identify and predict the diffusion of hate speech.

该项目将开发新的自然语言处理（NLP）方法和指标，用于自动预测在线仇恨言论的传播和破坏。尽管在过去十年中对在线仇恨言论进行了大量研究，但现有工作的一个关键限制是，重点是解决“仇恨在哪里”，而不是“仇恨将在哪里”。预测仇恨言论将在哪里出现、如何传播以及可能对在线社区造成多大程度的损害，对于有效利用有限的资源进行预防和干预至关重要，社交媒体公司和非政府组织每年投资数亿美元，但仍难以缓解网络仇恨。因此，该项目将为解决这一研究差距迈出宝贵的一步，并开发更有效的方法和工具来解决这一问题。该项目将基于计算和社会科学研究方法的结合。虽然这两个领域都研究了这个问题，但尽管综合方法会带来好处，但几乎没有交叉。该项目将：1）创建新的数据集，解决在线仇恨言论预测任务; 2）开发衡量标准，评估在线仇恨言论的损害及其对在线社区的损害; 3）开发新的NLP方法，以识别和预测仇恨言论的传播。