Publication Type
Working Paper
Version
publishedVersion
Publication Date
2-2022
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.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
First Page
1
Last Page
17
Identifier
10.48550/arXiv.2202.09517
Citation
MALIK, Jitendra Singh; PANG, Guansong; and HENGEL, Anton Van Den.
Deep learning for hate speech detection: A comparative study. (2022). 1-17.
Available at: https://ink.library.smu.edu.sg/sis_research/7015
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.