Generalisability of Topic Models in Cross-corpora Abusive Language Detection

Tulika Bose, Irina Illina, Dominique Fohr
2021 Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda   unpublished
Rapidly changing social media content calls for robust and generalisable abuse detection models. However, the state-of-the-art supervised models display degraded performance when they are evaluated on abusive comments that differ from the training corpus. We investigate if the performance of supervised models for cross-corpora abuse detection can be improved by incorporating additional information from topic models, as the latter can infer the latent topic mixtures from unseen samples. In
more » ... ular, we combine topical information with representations from a model tuned for classifying abusive comments. Our performance analysis reveals that topic models are able to capture abuse-related topics that can transfer across corpora, and result in improved generalisability.
doi:10.18653/v1/2021.nlp4if-1.8 fatcat:ddr65gqwpvdttiiw4lqfdp57ii