Explainable Abusive Language Classification Leveraging User and Network Data

Abstract

Online hate speech is a phenomenon with considerable consequences for our society. Its automatic detection using machine learning is a promising approach to contain its spread. However, classifying abusive language with a model that purely relies on text data is limited in performance due to the complexity and diversity of speech (e.g., irony, sarcasm). Moreover, studies have shown that a significant amount of hate on social media platforms stems from online hate communities. Therefore, we develop an abusive language detection model leveraging user and network data to improve the classification performance. We integrate the explainable AI framework SHAP (SHapley Additive exPlanations) to alleviate the general issue of missing transparency associated with deep learning models, allowing us to assess the model’s vulnerability toward bias and systematic discrimination reliably. Furthermore, we evaluate our multimodel architecture on three datasets in two languages (i.e., English and German). Our results show that user-specific timeline and network data can improve the classification, while the additional explanations resulting from SHAP make the predictions of the model interpretable to humans.