# Predictively Combatting Toxicity in Health-related Online Discussions through Machine Learning

arXiv: https://arxiv.org/abs/2505.17068 · HTML: https://mlsanity.com/paper/2505.17068/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 19 May 2025.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Collaborative Filtering vs. Matrix Factorization Revisited](https://mlsanity.com/paper/2005.09683/) (2020)
- [Exploring Data Splitting Strategies for the Evaluation of Recommendation Models](https://mlsanity.com/paper/2007.13237/) (2020)
- [Toxicity Detection is NOT all you Need: Measuring the Gaps to Supporting Volunteer Content Moderators](https://mlsanity.com/paper/2311.07879/) (2023)
- [Limitations of ROC on Imbalanced Data: Evaluation of LVAD Mortality Risk Scores](https://mlsanity.com/paper/2010.16253/) (2020)
- [Navigating the Post-API Dilemma | Search Engine Results Pages Present a Biased View of Social Media Data](https://mlsanity.com/paper/2401.15479/) (2024)

## Abstract (arXiv)

In health-related topics, user toxicity in online discussions frequently becomes a source of social conflict or promotion of dangerous, unscientific behaviour; common approaches for battling it include different forms of detection, flagging and/or removal of existing toxic comments, which is often counterproductive for platforms and users alike. In this work, we propose the alternative of combatting user toxicity predictively, anticipating where a user could interact toxically in health-related online discussions. Applying a Collaborative Filtering-based Machine Learning methodology, we predict the toxicity in COVID-related conversations between any user and subcommunity of Reddit, surpassing 80% predictive performance in relevant metrics, and allowing us to prevent the pairing of conflicting users and subcommunities.