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Online hate is a growing concern on many social media platforms and othersites. To combat it, technology companies are increasingly identifying andsanctioning `hateful users' rather than simply moderating hateful content. Yet,most research in online hate detection to date has focused on hateful content.This paper examines how fairer and more accurate hateful user detection systemscan be developed by incorporating social network information through geometricdeep learning. Geometric deep learning dynamically learns information-richnetwork representations and can generalise to unseen nodes. This is essentialfor moving beyond manually engineered network features, which lack scalabilityand produce information-sparse network representations. This paper compares theaccuracy of geometric deep learning with other techniques which either excludenetwork information or incorporate it through manual feature engineering (e.g.,node2vec). It also evaluates the fairness of these techniques using the`predictive equality' criteria, comparing the false positive rates on a subsetof 136 African-American users with 4836 other users. Geometric deep learningproduces the most accurate and fairest classifier, with an AUC score of 90.8\%on the entire dataset and a false positive rate of zero among theAfrican-American subset for the best performing model. This highlights thebenefits of more effectively incorporating social network features in automatedhateful user detection. Such an approach is also easily operationalized forreal-world content moderation as it has an efficient and scalable design.

Tackling Racial Bias in Automated Online Hate Detection: Towards Fair  and Accurate Classification of Hateful Online Users Using Geometric Deep  Learning