# "Stop Asian Hate!" : Refining Detection of Anti-Asian Hate Speech During the COVID-19 Pandemic

arXiv: https://arxiv.org/abs/2112.02265 · HTML: https://mlsanity.com/paper/2112.02265/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 55 references that are in the map.
- First posted on arXiv 4 Dec 2021.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations](https://mlsanity.com/paper/2110.05719/) (2021)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018)
- [Racial Bias in Hate Speech and Abusive Language Detection Datasets](https://mlsanity.com/paper/1905.12516/) (2019)
- [Demographic Dialectal Variation in Social Media: A Case Study of African-American English](https://mlsanity.com/paper/1608.08868/) (2016)
- [Directions in Abusive Language Training Data: Garbage In, Garbage Out](https://mlsanity.com/paper/2004.01670/) (2020)
- [A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media](https://mlsanity.com/paper/1910.12574/) (2019)
- [Challenges in Automated Debiasing for Toxic Language Detection](https://mlsanity.com/paper/2102.00086/) (2021)
- [Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators' Disagreement](https://mlsanity.com/paper/2109.13563/) (2021)
- [Towards generalisable hate speech detection: a review on obstacles and solutions](https://mlsanity.com/paper/2102.08886/) (2021)
- [Detecting East Asian Prejudice on Social Media](https://mlsanity.com/paper/2005.03909/) (2020)
- [Racism is a Virus: Anti-Asian Hate and Counterspeech in Social Media during the COVID-19 Crisis](https://mlsanity.com/paper/2005.12423/) (2020)
- [Detecting Hate Speech in Multi-modal Memes](https://mlsanity.com/paper/2012.14891/) (2020)
- [Mean Birds: Detecting Aggression and Bullying on Twitter](https://mlsanity.com/paper/1702.06877/) (2017)
- [Predicting Anti-Asian Hateful Users on Twitter during COVID-19](https://mlsanity.com/paper/2109.07296/) (2021)
- [Annotating for Hate Speech: The MaNeCo Corpus and Some Input from Critical Discourse Analysis](https://mlsanity.com/paper/2008.06222/) (2020)
- [Understanding Public Sentiments, Opinions and Topics about COVID-19 using Twitter](https://mlsanity.com/paper/2012.03039/) (2020)

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

- ["Define Your Terms" : Enhancing Efficient Offensive Speech Classification with Definition](https://mlsanity.com/paper/2402.03221/) (2024)
- [Hypothesis Engineering for Zero-Shot Hate Speech Detection](https://mlsanity.com/paper/2210.00910/) (2022)
- [How Much Hate with #china? A Preliminary Analysis on China-related Hateful Tweets Two Years After the Covid Pandemic Began](https://mlsanity.com/paper/2211.06116/) (2022)
- [Beyond Static Benchmarks: Synthesizing Harmful Content via Persona-based Simulation for Robust Evaluation](https://mlsanity.com/paper/2604.17020/) (2026)

## Abstract (arXiv)

Content warning: This work displays examples of explicit and/or strongly offensive language. Fueled by a surge of anti-Asian xenophobia and prejudice during the COVID-19 pandemic, many have taken to social media to express these negative sentiments. Identifying these posts is crucial for moderation and understanding the nature of hate in online spaces. In this paper, we create and annotate a corpus of tweets to explore anti-Asian hate speech with a finer level of granularity. Our analysis reveals that this emergent form of hate speech often eludes established approaches. To address this challenge, we develop a model and an accompanied efficient training regimen that incorporates agreement between annotators. Our approach produces up to 8.8% improvement in macro F1 scores over a strong established baseline, indicating its effectiveness even in settings where consensus among annotators is low. We demonstrate that we are able to identify hate speech that is systematically missed by established hate speech detectors.