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Text moderation for user generated content, which helps to promote healthyinteraction among users, has been widely studied and many machine learningmodels have been proposed. In this work, we explore an alternative perspectiveby augmenting reactive reviews with proactive forecasting. Specifically, wepropose a new concept {\it text toxicity propensity} to characterize the extentto which a text tends to attract toxic comments. Beta regression is thenintroduced to do the probabilistic modeling, which is demonstrated to functionwell in comprehensive experiments. We also propose an explanation method tocommunicate the model decision clearly. Both propensity scoring andinterpretation benefit text moderation in a novel manner. Finally, the proposedscaling mechanism for the linear model offers useful insights beyond this work.

BERT-Beta: A Proactive Probabilistic Approach to Text Moderation