Online Expressions Related to Muslim Identity and Islam: A Longitudinal Event Based Analysis

与穆斯林身份和伊斯兰教相关的在线表达：基于纵向事件的分析

• 批准号: 2273901
• 金额: --
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2273901
• 关键词: Online; Expressions; Related; Muslim; Identity

The use of AI to detect online hate speech on social media has gathered pace, however, automatic detection in itself does not allow for sociological insight. Although previous research has addressed the online propagation of cyberhate following trigger events, little is known about the longitudinal diffusion (e.g. years instead of days) of posts relating to Muslim identity. This limitation means we do not know if findings from cross-sectional research are generalisable across time, locations and events. This study aims to identify: i) how, when and where Muslim identity is being discussed on Twitter over 12- month period; ii) how Muslim identity is constructed; and iii) what social, content and temporal factors influence the production of anti-Muslim cyberhate. The student will be associated with the Labs and the existing PhD students conducting research on similar topics. The student will become involved in Lab based workshops and conferences, and will be encouraged to co-author journal articles with other students and staff.

使用人工智能来检测社交媒体上的在线仇恨言论已经加快了步伐，然而，自动检测本身并不允许社会学的洞察力。尽管以前的研究已经解决了触发事件后网络仇恨的在线传播问题，但对与穆斯林身份有关的帖子的纵向传播(例如，几年而不是几天)知之甚少。这一限制意味着我们不知道横断面研究的结果是否适用于不同时间、地点和事件。这项研究旨在确定：i)在12个月的时间里，穆斯林身份如何、何时、何地在Twitter上被讨论；ii)穆斯林身份是如何构建的；以及iii)哪些社会、内容和时间因素影响了反穆斯林网络仇恨的产生。学生将与实验室和现有的进行类似主题研究的博士生联系在一起。学生将参与以实验为基础的研讨会和会议，并将被鼓励与其他学生和教职员工共同撰写期刊文章。