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.
Full text not available from this repository.Abstract
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.
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Cyber-racism; Machine learning; Topic modelling; Sentiment analysis; Social media |
| Subjects: | R Medicine > R Medicine (General) T Technology > T Technology (General) |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Information System Faculty of Medicine |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 18 Oct 2023 08:37 |
| Last Modified: | 18 Oct 2023 08:37 |
| URI: | http://eprints.um.edu.my/id/eprint/42089 |
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