Online Hate Detection Systems: Challenges and Action Points for Developers, Data Scientists, and Researchers

Automated online hate detection has garnered interest from various stakeholders to make online platforms safer. Despite this interest, there remain a plethora of unresolved issues that hinder advancement. We review fourteen state-of-the-art articles discussing these challenges, and present a meta-synthesis. Six themes are identified: (1) Dataset selection, (2) Detection of False Positives and Negatives, (3) Semantic Context of Hate Messages, (4) Privacy and Anonymity, (5) Ethical Considerations, and (6) Minimizing Bias. For each theme, we provide a set of action points to support researchers, data scientists, and developers to improve hate detection systems.