Social sensing with big data: Detecting hate speech in social media

Umar Ibrahim 1, Usman Lawal Gulma 2, * and Ishaq Abdullahi Lawal 3

1 Department of Computer Science Education, Adamu Augie College of Education, P.M.B 1012, Argungu, Kebbi State, Nigeria.
2 Department of Geography, Adamu Augie College of Education, P.M.B 1012, Argungu, Kebbi State, Nigeria.
3 Department of General Studies Education, Adamu Augie College of Education, P.M.B 1012, Argungu, Kebbi State, Nigeria.
 
Research Article
International Journal of Science and Research Archive, 2024, 11(02), 1146–1152.
Article DOI: 10.30574/ijsra.2024.11.2.0540

 

Publication history: 
Received on 22 February 2024; revised on 29 March 2024; accepted on 01 April 2024
 
Abstract: 
The internet's accessibility and social media platforms, like Facebook and Twitter, have accelerated the spread of hate speech and fake news, both of which can be detrimental to society's overall well-being. Identifying and tracking hate speech is becoming increasingly difficult for the public, private citizens, legislators, and academics. Despite efforts to leverage automatic detection and monitoring techniques, their performances are still far from satisfactory. This study employs Natural Language Processing (NLP) and Machine Learning (ML) approaches to detect hate speech for decision-making. The result showed that the Support Vector Machine (SVM) algorithm has the best performance with an accuracy of 0.86 compared to the Random Forest with 0.8 accuracy. The manual evaluation of the performance of our algorithm yielded an inter-annotator agreement Cronbach’s alpha (α = .775).
 
Keywords: 
Hate speech; Natural language processing; Machine learning; Social sensing; Big data 
 
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0