The datafication of hate speech

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 re- quired by the datafication of hate to build such an automated system. The obser- vations 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 automa- ted 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.