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Unravelling social media racial discriminations through a semi-supervised approach

The study investigated cyber-racism on social media during the recent Coronavirus pandemic using a semi-supervised approach. Specifically, several machine learning models were trained to detect cyber-racism, followed by topic modelling using Latent Dirichlet Allocation (LDA). Twitter data were gathe...

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Main Authors: Balakrishnan, Vimala, Ng, Kee Seong, Arabnia, Hamid R.
Format: Article
Published: Elsevier 2022
Subjects:
R Medicine (General)
T Technology (General)
Online Access:http://eprints.um.edu.my/42089/
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spelling my.um.eprints.420892023-10-18T08:37:29Z http://eprints.um.edu.my/42089/ Unravelling social media racial discriminations through a semi-supervised approach Balakrishnan, Vimala Ng, Kee Seong Arabnia, Hamid R. R Medicine (General) T Technology (General) The study investigated cyber-racism on social media during the recent Coronavirus pandemic using a semi-supervised approach. Specifically, several machine learning models were trained to detect cyber-racism, followed by topic modelling using Latent Dirichlet Allocation (LDA). Twitter data were gathered using the hash tags Chinese virus and Kung Flu in the month of March 2020, resulting in 7,454 clean tweets. Negative tweets extracted using sentiment analysis were annotated (Racism, Sarcasm/irony and Others), and used to train several machine learning models. Experimental results show Random Forest with bagging to consistently outperform Random Forest, J48 and Support Vector Machine with an accuracy of 78.1% (Racism versus Sarcasm/Irony) and 77.9% (Racism versus Others). LDA revealed three distinct topics for tweets identified as racist, namely, Eating habit, Political hatred and Xenophobia. Consistent detection performance of the models evaluated indicate their reliability in detecting cyber-racism patterns based on textual communications. Elsevier 2022 Article PeerReviewed Balakrishnan, Vimala and Ng, Kee Seong and Arabnia, Hamid R. (2022) Unravelling social media racial discriminations through a semi-supervised approach. Telematics and Informatics, 67. ISSN 0736-5853, DOI https://doi.org/10.1016/j.tele.2021.101752 <https://doi.org/10.1016/j.tele.2021.101752>. 10.1016/j.tele.2021.101752
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic R Medicine (General)
T Technology (General)
spellingShingle R Medicine (General)
T Technology (General)
Balakrishnan, Vimala
Ng, Kee Seong
Arabnia, Hamid R.
Unravelling social media racial discriminations through a semi-supervised approach
description The study investigated cyber-racism on social media during the recent Coronavirus pandemic using a semi-supervised approach. Specifically, several machine learning models were trained to detect cyber-racism, followed by topic modelling using Latent Dirichlet Allocation (LDA). Twitter data were gathered using the hash tags Chinese virus and Kung Flu in the month of March 2020, resulting in 7,454 clean tweets. Negative tweets extracted using sentiment analysis were annotated (Racism, Sarcasm/irony and Others), and used to train several machine learning models. Experimental results show Random Forest with bagging to consistently outperform Random Forest, J48 and Support Vector Machine with an accuracy of 78.1% (Racism versus Sarcasm/Irony) and 77.9% (Racism versus Others). LDA revealed three distinct topics for tweets identified as racist, namely, Eating habit, Political hatred and Xenophobia. Consistent detection performance of the models evaluated indicate their reliability in detecting cyber-racism patterns based on textual communications.
format Article
author Balakrishnan, Vimala
Ng, Kee Seong
Arabnia, Hamid R.
author_facet Balakrishnan, Vimala
Ng, Kee Seong
Arabnia, Hamid R.
author_sort Balakrishnan, Vimala
title Unravelling social media racial discriminations through a semi-supervised approach
title_short Unravelling social media racial discriminations through a semi-supervised approach
title_full Unravelling social media racial discriminations through a semi-supervised approach
title_fullStr Unravelling social media racial discriminations through a semi-supervised approach
title_full_unstemmed Unravelling social media racial discriminations through a semi-supervised approach
title_sort unravelling social media racial discriminations through a semi-supervised approach
publisher Elsevier
publishDate 2022
url http://eprints.um.edu.my/42089/
_version_ 1781704593603821568
score 13.211869

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