Predictively Combatting Toxicity in Health-related Online Discussions through Machine Learning
원문 초록
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.
함께 살펴볼 논문
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NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
From ID-based to ID-free: Rethinking ID Effectiveness in Multimodal Collaborative Filtering Recommendation
RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendation
LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation
Dual-View Disentangled Multi-Intent Learning for Enhanced Collaborative Filtering
Discrete Scale-invariant Metric Learning for Efficient Collaborative Filtering
토론
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