The datafication of hate speech

Abstract

Hate speech has been identified as a pressing problem in society, and several automated approaches have been designed to detect and prevent it. This chapter reflects on the operationalizations, transformations, and reductions required by the datafication of hate to build such an automated system. The observations are based on an action research setting during a hate speech monitoring project conducted in a multi-organizational collaboration during the Finnish municipal elections in 2017. The project developed an adequately well-working algorithmic solution using supervised machine learning. However, the automated approach requires heavy simplification, such as using rudimentary scales for classifying hate speech and relying on word-based approaches, while in reality hate speech is a nuanced linguistic and social phenomenon with various tones and forms. The chapter concludes by suggesting some practical implications for developing hate speech recognition systems.