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.
The dataset used in this work was obtained using PushShift.io API, and consists of 1.5 million Reddit comments discussing COVID-19. These comments were posted between January and June of 2021 and were obtained during February of 2023. To preserve anonymicity, and due to the large dataset size, we currently only provide a temporary download link which will expire on 26/01/2026, and will be posted on Zenodo upon acceptance.
The provided framework was developed in Python 3.9 and uses CUDA 11.1 for GPU acceleration.
If set up correctly, python3 main.py should run the full pipeline, from data preprocessing to model evaluation.
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