Social media platforms may provide potential space for discourses that contain hate speech, and even worse, can act as a propagation mechanism for hate crimes. The FBI's Uniform Crime Reporting (UCR) Program collects hate crime data and releases statistic report yearly. These statistics provide information in determining national hate crime trends. The statistics can also provide valuable holistic and strategic insight for law enforcement agencies or justify lawmakers for specific legislation. However, the reports are mostly released next year and lag behind many immediate needs. Recent research mainly focuses on hate speech detection in social media text or empirical studies on the impact of a confirmed crime. This paper proposes a framework that first utilizes text mining techniques to extract hate crime events from New York Times news, then uses the results to facilitate predicting American national-level and state-level hate crime trends. Experimental results show that our method can significantly enhance the prediction performance compared with time series or regression methods without event-related factors. Our framework broadens the methods of national-level and state-level hate crime trends prediction.
翻译:联邦调查局的统一犯罪报告(UCR)方案每年收集仇恨犯罪数据并发布统计报告。这些统计数据为确定国家仇恨犯罪趋势提供了信息。统计数据还可以为执法机构提供宝贵的整体和战略见解,或为具体立法的立法机构提供依据。然而,这些报告大多在明年发布,落后于许多直接需求。最近的研究主要侧重于在社会媒体文本中发现仇恨言论,或对已证实犯罪的影响进行经验性研究。本文提出了一个框架,首先利用文字采矿技术从《纽约时报》新闻中提取仇恨犯罪事件,然后利用结果帮助预测美国国家一级和国家一级的仇恨犯罪趋势。实验结果表明,我们的方法可以大大提高预测业绩,而没有与事件相关的时间序列或回归方法。我们的框架扩大了国家一级和州一级的仇恨犯罪趋势预测方法。