了解在线反犹太主义的定量方法
A Quantitative Approach to Understanding Online Antisemitism
摘要：
在边缘网络社区的推动下，新一轮反犹太主义浪潮日益高涨，这在社会政治领域越来越令人担忧。网络的无处不在和全球性为这些团体提供了将其意识形态传播到互联网其他部分的工具。虽然反犹太主义和仇恨的研究并不新鲜，但在线数据的规模和变化率已经影响了传统方法衡量和理解这些令人不安的趋势的有效性。在本文中，我们提出了一个大规模的，定量研究在线反犹太主义。我们从另类右翼网络社区收集了数亿条帖子和图片，比如4chan的Politically Incorrect board（/pol/）和Gab。使用有科学依据的方法，我们量化了反犹太主义模因和修辞在网络上的升级和传播。我们发现，在2016年美国总统大选和夏洛茨维尔“团结右翼”集会等重大政治事件之后，反犹太主义内容的频率大大增加（在某些情况下增加了一倍以上）。我们从我们的帖子语料库中提取语义嵌入，并演示自动化技术如何发现和分类反犹太术语的使用。我们还研究了反犹太主义的流行和传播“快乐商人”模因，特别是这些边缘社区如何影响其传播到更主流的社区，如Twitter和Reddit。总之，我们的研究结果为理解在线反犹太主义提供了一个数据驱动的定量框架。我们的方法可以作为一个框架，增强反仇恨团体当前的定性工作，为在线仇恨的增长和传播提供新的见解。
英文摘要：
A new wave of growing antisemitism, driven by fringe Web communities, is an increasingly worrying presence in the socio-political realm. The ubiquitous and global nature of the Web has provided tools used by these groups to spread their ideology to the rest of the Internet. Although the study of antisemitism and hate is not new, the scale and rate of change of online data has impacted the efficacy of traditional approaches to measure and understand these troubling trends. In this paper, we present a large-scale, quantitative study of online antisemitism. We collect hundreds of million posts and images from alt-right Web communities like 4chan's Politically Incorrect board (/pol/) and Gab. Using scientifically grounded methods, we quantify the escalation and spread of antisemitic memes and rhetoric across the Web. We find the frequency of antisemitic content greatly increases (in some cases more than doubling) after major political events such as the 2016 US Presidential Election and the "Unite the Right" rally in Charlottesville. We extract semantic embeddings from our corpus of posts and demonstrate how automated techniques can discover and categorize the use of antisemitic terminology. We additionally examine the prevalence and spread of the antisemitic "Happy Merchant" meme, and in particular how these fringe communities influence its propagation to more mainstream communities like Twitter and Reddit. Taken together, our results provide a data-driven, quantitative framework for understanding online antisemitism. Our methods serve as a framework to augment current qualitative efforts by anti-hate groups, providing new insights into the growth and spread of hate online.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
