# Evolving Hate Speech Online: An Adaptive Framework for Detection and Mitigation

arXiv: https://arxiv.org/abs/2502.10921 · HTML: https://mlsanity.com/paper/2502.10921/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 63 references that are in the map.
- First posted on arXiv 15 Feb 2025.

## Builds on (most cited first, 21 of 21)

- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018)
- [Latent Hatred: A Benchmark for Understanding Implicit Hate Speech](https://mlsanity.com/paper/2109.05322/) (2021)
- [HateCheck: Functional Tests for Hate Speech Detection Models](https://mlsanity.com/paper/2012.15606/) (2020)
- [HateBERT: Retraining BERT for Abusive Language Detection in English](https://mlsanity.com/paper/2010.12472/) (2020)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [Neural Approaches to Conversational AI](https://mlsanity.com/paper/1809.08267/) (2018)
- [Deceiving Google's Perspective API Built for Detecting Toxic Comments](https://mlsanity.com/paper/1702.08138/) (2017)
- [A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media](https://mlsanity.com/paper/1910.12574/) (2019)
- [Statistically Significant Detection of Linguistic Change](https://mlsanity.com/paper/1411.3315/) (2014)
- [Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition](https://mlsanity.com/paper/2002.10361/) (2020)
- [A Quantitative Approach to Understanding Online Antisemitism](https://mlsanity.com/paper/1809.01644/) (2018)
- [Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two-path Bootstrapping Approach](https://mlsanity.com/paper/1710.07394/) (2017)
- [Kek, Cucks, and God Emperor Trump: A Measurement Study of 4chan's Politically Incorrect Forum and Its Effects on the Web](https://mlsanity.com/paper/1610.03452/) (2016)
- [Towards Hate Speech Detection at Large via Deep Generative Modeling](https://mlsanity.com/paper/2005.06370/) (2020)
- [A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research](https://mlsanity.com/paper/1802.09416/) (2018)
- [Reducing Unintended Identity Bias in Russian Hate Speech Detection](https://mlsanity.com/paper/2010.11666/) (2020)
- [Generative AI for Hate Speech Detection: Evaluation and Findings](https://mlsanity.com/paper/2311.09993/) (2023)
- ["Go eat a bat, Chang!": On the Emergence of Sinophobic Behavior on Web Communities in the Face of COVID-19](https://mlsanity.com/paper/2004.04046/) (2020)
- [Generative AI may backfire for counterspeech](https://mlsanity.com/paper/2411.14986/) (2024)
- [Hate is not Binary: Studying Abusive Behavior of #GamerGate on Twitter](https://mlsanity.com/paper/1705.03345/) (2017)

## Built on by (most cited first, 1 of 1)

- [Beyond Static Benchmarks: Synthesizing Harmful Content via Persona-based Simulation for Robust Evaluation](https://mlsanity.com/paper/2604.17020/) (2026)

## Abstract (arXiv)

The proliferation of social media platforms has led to an increase in the spread of hate speech, particularly targeting vulnerable communities. Unfortunately, existing methods for automatically identifying and blocking toxic language rely on pre-constructed lexicons, making them reactive rather than adaptive. As such, these approaches become less effective over time, especially when new communities are targeted with slurs not included in the original datasets. To address this issue, we present an adaptive approach that uses word embeddings to update lexicons and develop a hybrid model that adjusts to emerging slurs and new linguistic patterns. This approach can effectively detect toxic language, including intentional spelling mistakes employed by aggressors to avoid detection. Our hybrid model, which combines BERT with lexicon-based techniques, achieves an accuracy of 95% for most state-of-the-art datasets. Our work has significant implications for creating safer online environments by improving the detection of toxic content and proactively updating the lexicon. Content Warning: This paper contains examples of hate speech that may be triggering.