Understanding Abuse: A Typology of Abusive Language Detection Subtasks
This work provides a foundational typology for researchers in abusive language detection, though it is incremental as it synthesizes existing knowledge without introducing new methods or data.
The paper addresses the need for a structured framework to differentiate abusive language detection subtasks, proposing a typology based on existing research to guide data annotation and feature construction.
As the body of research on abusive language detection and analysis grows, there is a need for critical consideration of the relationships between different subtasks that have been grouped under this label. Based on work on hate speech, cyberbullying, and online abuse we propose a typology that captures central similarities and differences between subtasks and we discuss its implications for data annotation and feature construction. We emphasize the practical actions that can be taken by researchers to best approach their abusive language detection subtask of interest.