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

--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. The hierarchical and decentralised structure made Reddit a hub of heated debate during the onset of the COVID pandemic, with over 200,000 related posts per day. Center accredited by Galician University System, is funded by "Conseller Conversely, volunteer-based moderation is generally more susceptible to bias and under-moderation, depending on the platform's audience. The design of an adapted Leave Out Last Item data partitioning method suitable for binary classification-oriented Collaborative Filtering tasks. We remove "generic comments'' from the set, i.e. those Label comments as "generic'' if they do not contain any words from Authors have temporarily removed this link to the work's repository to The majority of users do not post toxic comments when discussing health on Reddit, with 9.96% of toxic comments in the aggregate, similar to previous work. Furthermore, as Figure 2 shows, a user's toxicity on a subreddit tends to be consistent (toxic or non-toxic, as indicated by the peaks in the distribution at toxicities 0 Note the logarithmic scale on the y-axis. To tag the toxicity of comments we use Detoxify-original [7], a pre-trained language model. Instead of only detecting and punishing the toxicity of existing interactions like common content moderation methods, which is ineffective and counterproductive in the long term, this work's proposal is to predict the toxicity of an unobserved interaction Figure 5. Topology of the Machine Learning model proposed to predict the toxicity of health-related conversations in unobserved user-subreddit interactions on the Reddit platform. We assessed the predictive ability of our model and baselines using classical binary classification metrics: sensitivity, specificity, and geometric mean (G.Mean) of the class-wise We identify different avenues of future work. U. Naseem, J. Kim, M. Khushi, and A. G. Dunn, "Identification of disease or symptom terms in reddit to improve health mention classification," in "R/redditsecurity - understanding hate on reddit, and the impact of our Iii, "Toxicity detection is not all you need: Measuring the gaps to "Meta to replace'biased' fact-checkers with moderation by users -- J. Brownlee, Imbalanced classification with Python: better metrics, balance skewed classes, cost-sensitive learning .