Unravelling social media racial discriminations through a semi-supervised approach
Document Type
Article
Publication Date
1-1-2022
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.
Keywords
Cyber-racism, Machine learning, Topic modelling, Sentiment analysis, Social media
Divisions
infosystem,fac_med
Funders
None
Publication Title
Telematics and Informatics
Volume
67
Publisher
Elsevier
Publisher Location
RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS