Online hate speech proliferation has created a difficult problem for social media platforms. A particular challenge relates to the use of coded language by groups interested in both creating a sense of belonging for its users and evading detection. Coded language evolves quickly and its use varies over time. This paper proposes a methodology for detecting emerging coded hate-laden terminology. The methodology is tested in the context of online antisemitic discourse. The approach considers posts scraped from social media platforms, often used by extremist users. The posts are scraped using seed expressions related to previously known discourse of hatred towards Jews. The method begins by identifying the expressions most representative of each post and calculating their frequency in the whole corpus. It filters out grammatically incoherent expressions as well as previously encountered ones so as to focus on emergent well-formed terminology. This is followed by an assessment of semantic similarity to known antisemitic terminology using a fine-tuned large language model, and subsequent filtering out of the expressions that are too distant from known expressions of hatred. Emergent antisemitic expressions containing terms clearly relating to Jewish topics are then removed to return only coded expressions of hatred.

翻译：在线仇恨言论的激增给社交媒体平台带来了棘手问题。一个特殊挑战在于：某些群体为营造归属感并规避检测而使用编码语言。这类编码语言演变迅速，其具体运用方式亦随时间推移而变化。本文提出了一种检测新兴编码仇恨术语的方法，并在网络反犹言论语境下进行验证。该方法以极端用户常用的社交媒体平台抓取帖子为样本，使用与已知反犹言论相关的种子表达式进行抓取。研究首先识别每篇帖子最具代表性的表达，计算其在整个语料库中的出现频率，过滤掉语法不通顺的表达及已知术语，从而聚焦于新出现的高质量术语。随后通过微调大语言模型评估这些术语与已知反犹术语的语义相似度，进一步过滤与已知仇恨表达语义距离过远的术语。最后移除明确涉及犹太主题的新兴反犹表达，仅保留编码形式的仇恨术语。