# Cyberbullying Detection with Fairness Constraints

arXiv: https://arxiv.org/abs/2005.06625 · HTML: https://mlsanity.com/paper/2005.06625/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2020.

## Built on by (most cited first, 15 of 15)

- [Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics](https://mlsanity.com/paper/2106.14574/) (2021)
- [Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling](https://mlsanity.com/paper/2107.03451/) (2021)
- [Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on Toxicity Annotation](https://mlsanity.com/paper/2205.00501/) (2022)
- [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022)
- [Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities](https://mlsanity.com/paper/2102.04257/) (2021)
- [How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing](https://mlsanity.com/paper/2205.10095/) (2022)
- [Session-based Cyberbullying Detection in Social Media: A Survey](https://mlsanity.com/paper/2207.10639/) (2022)
- [End-To-End Bias Mitigation: Removing Gender Bias in Deep Learning](https://mlsanity.com/paper/2104.02532/) (2021)
- [Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations](https://mlsanity.com/paper/2201.06384/) (2022)
- [Toward Understanding Bias Correlations for Mitigation in NLP](https://mlsanity.com/paper/2205.12391/) (2022)
- [Toxicity in Online Platforms and AI Systems: A Survey of Needs, Challenges, Mitigations, and Future Directions](https://mlsanity.com/paper/2509.25539/) (2025)
- [UPB at SemEval-2021 Task 5: Virtual Adversarial Training for Toxic Spans Detection](https://mlsanity.com/paper/2104.08635/) (2021)
- [ID-XCB: Data-independent Debiasing for Fair and Accurate Transformer-based Cyberbullying Detection](https://mlsanity.com/paper/2402.16458/) (2024)
- [#ContextMatters: Advantages and Limitations of Using Machine Learning to Support Women in Politics](https://mlsanity.com/paper/2110.00116/) (2021)
- [Penalized Fair Regression for Multiple Groups in Chronic Kidney Disease](https://mlsanity.com/paper/2512.17340/) (2025)

## Abstract (arXiv)

Cyberbullying is a widespread adverse phenomenon among online social interactions in today's digital society. While numerous computational studies focus on enhancing the cyberbullying detection performance of machine learning algorithms, proposed models tend to carry and reinforce unintended social biases. In this study, we try to answer the research question of "Can we mitigate the unintended bias of cyberbullying detection models by guiding the model training with fairness constraints?". For this purpose, we propose a model training scheme that can employ fairness constraints and validate our approach with different datasets. We demonstrate that various types of unintended biases can be successfully mitigated without impairing the model quality. We believe our work contributes to the pursuit of unbiased, transparent, and ethical machine learning solutions for cyber-social health.