# Automated Identification of Toxic Code Reviews Using ToxiCR

arXiv: https://arxiv.org/abs/2202.13056 · HTML: https://mlsanity.com/paper/2202.13056/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 109 references that are in the map.
- First posted on arXiv 26 Feb 2022.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020)
- [A Computational Approach to Politeness with Application to Social Factors](https://mlsanity.com/paper/1306.6078/) (2013)
- [HDLTex: Hierarchical Deep Learning for Text Classification](https://mlsanity.com/paper/1709.08267/) (2017)
- [Deceiving Google's Perspective API Built for Detecting Toxic Comments](https://mlsanity.com/paper/1702.08138/) (2017)
- [Conversations Gone Awry: Detecting Early Signs of Conversational Failure](https://mlsanity.com/paper/1805.05345/) (2018)
- [DocBERT: BERT for Document Classification](https://mlsanity.com/paper/1904.08398/) (2019)
- [Designing Toxic Content Classification for a Diversity of Perspectives](https://mlsanity.com/paper/2106.04511/) (2021)
- [Demoting Racial Bias in Hate Speech Detection](https://mlsanity.com/paper/2005.12246/) (2020)
- [Challenges for Toxic Comment Classification: An In-Depth Error Analysis](https://mlsanity.com/paper/1809.07572/) (2018)
- [Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification](https://mlsanity.com/paper/1812.07683/) (2018)
- [Convolutional Neural Networks for Toxic Comment Classification](https://mlsanity.com/paper/1802.09957/) (2018)
- [Towards Robust Toxic Content Classification](https://mlsanity.com/paper/1912.06872/) (2019)
- [A Benchmark Study on Sentiment Analysis for Software Engineering Research](https://mlsanity.com/paper/1803.06525/) (2018)
- [Neural Character-based Composition Models for Abuse Detection](https://mlsanity.com/paper/1809.00378/) (2018)
- [Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks](https://mlsanity.com/paper/1609.08409/) (2016)
- [EmoTxt: A Toolkit for Emotion Recognition from Text](https://mlsanity.com/paper/1708.03892/) (2017)
- [Bug or Not? Bug Report Classification Using N-Gram IDF](https://mlsanity.com/paper/1709.05763/) (2017)
- [A Benchmark Study of the Contemporary Toxicity Detectors on Software Engineering Interactions](https://mlsanity.com/paper/2009.09331/) (2020)
- [Expressions of Sentiments During Code Reviews: Male vs. Female](https://mlsanity.com/paper/1812.05560/) (2018)
- [The "Shut the f**k up" Phenomenon: Characterizing Incivility in Open Source Code Review Discussions](https://mlsanity.com/paper/2108.09905/) (2021)

## Built on by (most cited first, 8 of 8)

- [Software Vulnerability and Functionality Assessment using LLMs](https://mlsanity.com/paper/2403.08429/) (2024)
- [CRScore: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smells](https://mlsanity.com/paper/2409.19801/) (2024)
- [Combating Toxic Language: A Review of LLM-Based Strategies for Software Engineering](https://mlsanity.com/paper/2504.15439/) (2025)
- [Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review](https://mlsanity.com/paper/2605.17548/) (2026)
- [On the Challenges of Building Datasets for Hate Speech Detection](https://mlsanity.com/paper/2309.02912/) (2023)
- [Web(er) of Hate: A Survey on How Hate Speech Is Typed](https://mlsanity.com/paper/2506.16190/) (2025)
- [Machine Learning Models for the Early Detection of Burnout in Software Engineering: a Systematic Literature Review](https://mlsanity.com/paper/2603.23063/) (2026)
- [From Specialization to Generalization: Instruction-tuned LLMs for Robust Harmful Content Mitigation](https://mlsanity.com/paper/2608.25605/) (2026)

## Abstract (arXiv)

Toxic conversations during software development interactions may have serious repercussions on a Free and Open Source Software (FOSS) development project. For example, victims of toxic conversations may become afraid to express themselves, therefore get demotivated, and may eventually leave the project. Automated filtering of toxic conversations may help a FOSS community to maintain healthy interactions among its members. However, off-the-shelf toxicity detectors perform poorly on Software Engineering (SE) datasets, such as one curated from code review comments. To encounter this challenge, we present ToxiCR, a supervised learning-based toxicity identification tool for code review interactions. ToxiCR includes a choice to select one of the ten supervised learning algorithms, an option to select text vectorization techniques, eight preprocessing steps, and a large-scale labeled dataset of 19,571 code review comments. Two out of those eight preprocessing steps are SE domain specific. With our rigorous evaluation of the models with various combinations of preprocessing steps and vectorization techniques, we have identified the best combination for our dataset that boosts 95.8% accuracy and 88.9% F1 score. ToxiCR significantly outperforms existing toxicity detectors on our dataset. We have released our dataset, pre-trained models, evaluation results, and source code publicly available at: https://github.com/WSU-SEAL/ToxiCR