Hate in the Machine: Anti-Black and Anti-Muslim Social Media Posts as Predictors of Offline Racially and Religiously Aggravated Crime
Hate in the Machine: Anti-Black and Anti-Muslim Social Media Posts as Predictors of Offline Racially and Religiously Aggravated Crime
复制标题
机器中的仇恨：反黑人和反穆斯林社交媒体帖子是线下种族和宗教严重犯罪的预测因素
DOI：
--
复制
发表时间：
2019
影响因子：
2.6
通讯作者：
Sefa Ozalp
中科院分区：
文献类型：
--
作者：
M. Williams;P. Burnap;Amir Javed;Han Liu;Sefa Ozalp
各国政府现在认识到网络仇恨言论是一个有害的社会问题。在政治投票和恐怖袭击之后，众所周知，在线和离线的仇恨事件会同时达到顶峰。本文探讨了这两种形式的仇恨之间是否存在关联，独立于“触发”事件。利用利用数据科学方法的计算犯罪学，我们将警方犯罪，人口普查和Twitter数据联系起来，以建立针对种族和宗教的在线仇恨言论与伦敦8个月内的离线种族和宗教加重犯罪之间的时空关联。这些发现更新了我们对仇恨犯罪的理解，将其视为数字时代的一个过程，而不是一个离散事件。
National governments now recognize online hate speech as a pernicious social problem. In the wake of political votes and terror attacks, hate incidents online and offline are known to peak in tandem. This article examines whether an association exists between both forms of hate, independent of ‘trigger’ events. Using Computational Criminology that draws on data science methods, we link police crime, census and Twitter data to establish a temporal and spatial association between online hate speech that targets race and religion, and offline racially and religiously aggravated crimes in London over an eight-month period. The findings renew our understanding of hate crime as a process, rather than as a discrete event, for the digital age.
影响因子：
4.9
作者：
Burnap, Pete;Williams, Matthew L.
通讯作者：
Williams, Matthew L.