Using LLMs to discover emerging coded antisemitic hate-speech in extremist social media

TL;DR

This paper introduces a method leveraging large language models to detect emerging coded antisemitic hate speech in social media by filtering and assessing semantic similarity to known hate expressions.

Contribution

The study presents a novel approach combining frequency analysis and semantic similarity filtering to identify new coded hate speech expressions in online discourse.

Findings

Effective detection of emergent antisemitic coded language.

Reduction of false positives by filtering semantically distant expressions.

Identification of new hate terms related to Jewish topics.

Abstract

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.