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

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).