Publication Type
Journal Article
Version
acceptedVersion
Publication Date
10-2025
Abstract
Automated hate speech detection is an important tool in combating the spread of hate speech, particularly in social media. Numerous methods have been developed for the task, including a recent proliferation of deep-learning based approaches. A variety of datasets have also been developed, exemplifying various manifestations of the hate-speech detection problem. We present here a largescale empirical comparison of deep and shallow hate-speech detection methods, mediated through the three most commonly used datasets. Our goal is to illuminate progress in the area, and identify strengths and weaknesses in the current state-of-the-art. We particularly focus our analysis on measures of practical performance, including detection accuracy, computational efficiency, capability in using pre-trained models, and domain generalization. In doing so we aim to provide guidance as to the use of hate-speech detection in practice, quantify the state-of-the-art, and identify future research directions.
Keywords
Hate speech detection, Natural language processing, Deep learning, Machine learning, Transformers
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
International Journal of Data Science and Analytics
Volume
20
First Page
3055
Last Page
3068
ISSN
2364-415X
Identifier
10.1007/s41060-024-00650-6
Publisher
Springer
Citation
MALIK, Jitendra Singh; QIAO, Hezhe; PANG, Guansong; and HENGEL, Anton Van Den.
Deep learning for hate speech detection: A comparative study. (2025). International Journal of Data Science and Analytics. 20, 3055-3068.
Available at: https://ink.library.smu.edu.sg/sis_research/7015
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s41060-024-00650-6