# Detecting the Hate Code on Social Media

arXiv: https://arxiv.org/abs/1703.05443 · HTML: https://mlsanity.com/paper/1703.05443/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2017.

## Built on by (most cited first, 16 of 16)

- [Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Model](https://mlsanity.com/paper/2111.01515/) (2021)
- [From Dogwhistles to Bullhorns: Unveiling Coded Rhetoric with Language Models](https://mlsanity.com/paper/2305.17174/) (2023)
- [AOMD: An Analogy-aware Approach to Offensive Meme Detection on Social Media](https://mlsanity.com/paper/2106.11229/) (2021)
- [Interpretable Multi-Modal Hate Speech Detection](https://mlsanity.com/paper/2103.01616/) (2021)
- [Deciphering Implicit Hate: Evaluating Automated Detection Algorithms for Multimodal Hate](https://mlsanity.com/paper/2106.05903/) (2021)
- [Silent Signals, Loud Impact: LLMs for Word-Sense Disambiguation of Coded Dog Whistles](https://mlsanity.com/paper/2406.06840/) (2024)
- [Evaluating ChatGPT's Performance for Multilingual and Emoji-based Hate Speech Detection](https://mlsanity.com/paper/2305.13276/) (2023)
- [IIITG-ADBU@HASOC-Dravidian-CodeMix-FIRE2020: Offensive Content Detection in Code-Mixed Dravidian Text](https://mlsanity.com/paper/2107.14336/) (2021)
- [Safer Conversational AI as a Source of User Delight](https://mlsanity.com/paper/2304.09865/) (2023)
- [Going Extreme: Comparative Analysis of Hate Speech in Parler and Gab](https://mlsanity.com/paper/2201.11770/) (2022)
- [Code Word Detection in Fraud Investigations using a Deep-Learning Approach](https://mlsanity.com/paper/2103.09606/) (2021)
- [Detecting Offensive Language on Social Networks: An End-to-end Detection Method based on Graph Attention Networks](https://mlsanity.com/paper/2203.02123/) (2022)
- [A Group-Specific Approach to NLP for Hate Speech Detection](https://mlsanity.com/paper/2304.11223/) (2023)
- [Cordyceps@LT-EDI: Patching Language-Specific Homophobia/Transphobia Classifiers with a Multilingual Understanding](https://mlsanity.com/paper/2309.13561/) (2023)
- [Hate Content Detection via Novel Pre-Processing Sequencing and Ensemble Methods](https://mlsanity.com/paper/2409.05134/) (2024)
- [Covering Cracks in Content Moderation: Delexicalized Distant Supervision for Illicit Drug Jargon Detection](https://mlsanity.com/paper/2503.14926/) (2025)

## Abstract (arXiv)

Social media has become an indispensable part of the everyday lives of millions of people around the world. It provides a platform for expressing opinions and beliefs, communicated to a massive audience. However, this ease with which people can express themselves has also allowed for the large scale spread of propaganda and hate speech. To prevent violating the abuse policies of social media platforms and also to avoid detection by automatic systems like Google's Conversation AI, racists have begun to use a code (a movement termed Operation Google). This involves substituting references to communities by benign words that seem out of context, in hate filled posts or Tweets. For example, users have used the words Googles and Bings to represent the African-American and Asian communities, respectively. By generating the list of users who post such content, we move a step forward from classifying tweets by allowing us to study the usage pattern of these concentrated set of users.