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DOI: 10.14569/IJACSA.2022.01308100
PDF

A New Hate Speech Detection System based on Textual and Psychological Features

Author 1: Fatimah Alkomah
Author 2: Sanaz Salati
Author 3: Xiaogang Ma

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 8, 2022.

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Abstract: Hate speech often spreads on social media and harms individuals and the community. Machine learning models have been proposed to detect hate speech in social media; however, several issues presently limit the performance of current approaches. One challenge is the issue of having diverse comprehensions of hate speech constructs which will lead to many speech categories and different interpretations. In addition, certain language-specific features, and short text issues, such as Twitter, exacerbate the problem. Moreover, current machine learning approaches lack universality due to small datasets and the adoption of a few features of hateful speech. This paper develops and builds new feature sets based on frequencies of textual tokens and psychological characteristics. Then, the study evaluates several machine learning methods over a large dataset. Results showed that the Random Forest and BERT methods are the most valuable for detecting hate speech content. Furthermore, the most dominant features that are helpful for hate speech detection methods combine psychological features and Term-Frequency Inverse Document-Frequency (TFIDF) features. Therefore, the proposed approach could identify hate speech on social media platforms like Twitter.

Keywords: Hate speech detection; hate speech classification; hate speech features; hate speech methods

Fatimah Alkomah, Sanaz Salati and Xiaogang Ma, “A New Hate Speech Detection System based on Textual and Psychological Features” International Journal of Advanced Computer Science and Applications(IJACSA), 13(8), 2022. http://dx.doi.org/10.14569/IJACSA.2022.01308100

@article{Alkomah2022,
title = {A New Hate Speech Detection System based on Textual and Psychological Features},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.01308100},
url = {http://dx.doi.org/10.14569/IJACSA.2022.01308100},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {8},
author = {Fatimah Alkomah and Sanaz Salati and Xiaogang Ma}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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