# Designing Toxic Content Classification for a Diversity of Perspectives

arXiv: https://arxiv.org/abs/2106.04511 · HTML: https://mlsanity.com/paper/2106.04511/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2021.

## Built on by (most cited first, 30 of 65)

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- [Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting](https://mlsanity.com/paper/2309.07034/) (2023)
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- [Data Feedback Loops: Model-driven Amplification of Dataset Biases](https://mlsanity.com/paper/2209.03942/) (2022)
- [The Ecological Fallacy in Annotation: Modelling Human Label Variation goes beyond Sociodemographics](https://mlsanity.com/paper/2306.11559/) (2023)
- [You Are What You Annotate: Towards Better Models through Annotator Representations](https://mlsanity.com/paper/2305.14663/) (2023)
- [Validating Large Language Models with ReLM](https://mlsanity.com/paper/2211.15458/) (2022)
- [Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptions](https://mlsanity.com/paper/2502.20897/) (2025)
- [Value Profiles for Encoding Human Variation](https://mlsanity.com/paper/2503.15484/) (2025)
- [Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making](https://mlsanity.com/paper/2302.06503/) (2023)
- [Annotation alignment: Comparing LLM and human annotations of conversational safety](https://mlsanity.com/paper/2406.06369/) (2024)
- [Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive](https://mlsanity.com/paper/2301.12534/) (2023)
- [GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectives](https://mlsanity.com/paper/2311.05074/) (2023)
- [Model Sketching: Centering Concepts in Early-Stage Machine Learning Model Design](https://mlsanity.com/paper/2303.02884/) (2023)
- [LLM Roleplay: Simulating Human-Chatbot Interaction](https://mlsanity.com/paper/2407.03974/) (2024)
- [Watch Your Language: Investigating Content Moderation with Large Language Models](https://mlsanity.com/paper/2309.14517/) (2023)
- [Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks](https://mlsanity.com/paper/2112.07475/) (2021)
- [D3CODE: Disentangling Disagreements in Data across Cultures on Offensiveness Detection and Evaluation](https://mlsanity.com/paper/2404.10857/) (2024)
- [Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models](https://mlsanity.com/paper/2507.13383/) (2025)
- ["Cold, Calculated, and Condescending": How AI Identifies and Explains Ableism Compared to Disabled People](https://mlsanity.com/paper/2410.03448/) (2024)
- [Exploring Multimodal Challenges in Toxic Chinese Detection: Taxonomy, Benchmark, and Findings](https://mlsanity.com/paper/2505.24341/) (2025)
- [Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectives](https://mlsanity.com/paper/2508.02853/) (2025)
- [Automated Identification of Toxic Code Reviews Using ToxiCR](https://mlsanity.com/paper/2202.13056/) (2022)
- [Corpus Considerations for Annotator Modeling and Scaling](https://mlsanity.com/paper/2404.02340/) (2024)
- [Rater Cohesion and Quality from a Vicarious Perspective](https://mlsanity.com/paper/2408.08411/) (2024)
- [Re-examining Sexism and Misogyny Classification with Annotator Attitudes](https://mlsanity.com/paper/2410.03543/) (2024)

## Abstract (arXiv)

In this work, we demonstrate how existing classifiers for identifying toxic comments online fail to generalize to the diverse concerns of Internet users. We survey 17,280 participants to understand how user expectations for what constitutes toxic content differ across demographics, beliefs, and personal experiences. We find that groups historically at-risk of harassment - such as people who identify as LGBTQ+ or young adults - are more likely to to flag a random comment drawn from Reddit, Twitter, or 4chan as toxic, as are people who have personally experienced harassment in the past. Based on our findings, we show how current one-size-fits-all toxicity classification algorithms, like the Perspective API from Jigsaw, can improve in accuracy by 86% on average through personalized model tuning. Ultimately, we highlight current pitfalls and new design directions that can improve the equity and efficacy of toxic content classifiers for all users.