Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words

Joan Serrà, Ilias Leontiadis, Dimitris Spathis, Gianluca Stringhini, Jeremy Blackburn, Athena Vakali

Abstract
Common approaches to text categorization essentially rely either on n-gram counts or on word embeddings. This presents important difficulties in highly dynamic or quickly-interacting environments, where the appearance of new words and/or varied misspellings is the norm. A paradigmatic example of this situation is abusive online behavior, with social networks and media platforms struggling to effectively combat uncommon or non-blacklisted hate words. To better deal with these issues in those fast-paced environments, we propose using the error signal of class-based language models as input to text classification algorithms. In particular, we train a next-character prediction model for any given class and then exploit the error of such class-based models to inform a neural network classifier. This way, we shift from the ‘ability to describe’ seen documents to the ‘ability to predict’ unseen content. Preliminary studies using out-of-vocabulary splits from abusive tweet data show promising results, outperforming competitive text categorization strategies by 4-11%.
Anthology ID:
W17-3005
Volume:
Proceedings of the First Workshop on Abusive Language Online
Month:
August
Year:
2017
Address:
Vancouver, BC, Canada
Editors:
Zeerak Waseem, Wendy Hui Kyong Chung, Dirk Hovy, Joel Tetreault
Venue:
ALW
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
36–40
Language:
URL:
https://aclanthology.org/W17-3005
DOI:
10.18653/v1/W17-3005
Bibkey:
Cite (ACL):
Joan Serrà, Ilias Leontiadis, Dimitris Spathis, Gianluca Stringhini, Jeremy Blackburn, and Athena Vakali. 2017. Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words. In Proceedings of the First Workshop on Abusive Language Online, pages 36–40, Vancouver, BC, Canada. Association for Computational Linguistics.
Cite (Informal):
Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words (Serrà et al., ALW 2017)
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https://preview.aclanthology.org/ingest-acl-2023-videos/W17-3005.pdf
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