Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2024

Abstract

Detecting hate speech on social media poses a significant challenge, especially in distinguishing it from offensive language, as learning-based models often struggle due to nuanced differences between them, which leads to frequent misclassifications of hate speech instances, with most research focusing onrefining hate speech detection methods. Thus, this paper seeks to know if traditional learning-based methods should still be used, considering the perceived advantages of deep learning in this domain. This is done by investigating advancements in hate speech detection. It involves the utilization of deep learning-based models for detailed hate speech detection tasks and compares the results with those obtained from traditional learning-based baseline models through multidimensional aspect analysis. By consideringvarious aspects to gain a comprehensive understanding, we can discern the strengths and weaknesses in current state-of-theart techniques. Our research findings reveal the performance of traditional learning-based hate speech detection outperforms that of deep learning-based methods. While acknowledging thepotential demonstrated by deep learning methodologies, this study emphasizes the significance of traditional machine learning approaches in effectively addressing hate speech detection tasks. It advocates for a balanced perspective, highlighting that dismissing the capabilities of traditional methods in favor of emerging deep learning-based techniques may not consistently yield the most effective results.

Keywords

Deep learning, Hate speech detection, Performance comparison, Traditional learning-based methods, Multidimensional aspect analysis

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

Proceedings of the 2024 IEEE Conference on Artificial Intelligence (IEEE CAI 2024), Singapore, June 25-27

First Page

1

Last Page

5

Publisher

IEEE

City or Country

Los Alamitos, CA

Copyright Owner and License

Authors

Download

Share

COinS