Improved Abusive Comment Moderation with User Embeddings

John Pavlopoulos, Prodromos Malakasiotis, Juli Bakagianni, Ion Androutsopoulos

Experimenting with a dataset of approximately 1.6M user comments from a Greek news sports portal, we explore how a state of the art RNN-based moderation method can be improved by adding user embeddings, user type embeddings, user biases, or user type biases. We observe improvements in all cases, with user embeddings leading to the biggest performance gains.

Knowledge Graph



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