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

TL;DR

This paper introduces a machine learning approach that predicts potential toxicity in health-related online discussions, enabling proactive moderation to prevent conflicts before they occur.

Contribution

It presents a collaborative filtering-based method to predict toxicity in COVID-related Reddit discussions, surpassing 80% accuracy and enabling preemptive moderation.

Findings

Achieved over 80% predictive performance in toxicity detection

Enabled prevention of toxic user interactions in online health discussions

Demonstrated effectiveness on Reddit COVID conversations

Abstract

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.