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

arXiv: https://arxiv.org/abs/2401.10841 · HTML: https://mlsanity.com/paper/2401.10841/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [A Unified Deep Learning Architecture for Abuse Detection](https://mlsanity.com/paper/1802.00385/) (2018)
- [A Quantitative Approach to Understanding Online Antisemitism](https://mlsanity.com/paper/1809.01644/) (2018)
- ["Subverting the Jewtocracy": Online Antisemitism Detection Using Multimodal Deep Learning](https://mlsanity.com/paper/2104.05947/) (2021)
- [UHH-LT at SemEval-2020 Task 12: Fine-Tuning of Pre-Trained Transformer Networks for Offensive Language Detection](https://mlsanity.com/paper/2004.11493/) (2020)
- [Annotating Antisemitic Online Content. Towards an Applicable Definition of Antisemitism](https://mlsanity.com/paper/1910.01214/) (2019)
- [Antisemitic Messages? A Guide to High-Quality Annotation and a Labeled Dataset of Tweets](https://mlsanity.com/paper/2304.14599/) (2023)

## Built on by (most cited first, 4 of 4)

- [Large language models for newspaper sentiment analysis during COVID-19: The Guardian](https://mlsanity.com/paper/2405.13056/) (2024)
- [Evaluating Large Language Models for Detecting Antisemitism](https://mlsanity.com/paper/2509.18293/) (2025)
- [Monitoring the evolution of antisemitic discourse on extremist social media using BERT](https://mlsanity.com/paper/2403.05548/) (2024)
- [Fine-Tuning Large Language Models with QLoRA for Offensive Language Detection in Roman Urdu-English Code-Mixed Text](https://mlsanity.com/paper/2510.03683/) (2025)

## Abstract (arXiv)

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.