Hate Speech Detection in Code-switched Text Messages

Abstract

Not only does it happen in America, but also in Asia, in Africa and all over the world: Hate Speech. The exponential growth of user-generated content on social media bordering hate speech is increasingly alarming. Several efforts to monitor this phenomenon by social media network companies and the research community are on-going with various degrees of success. One gap in previous studies that this study addresses is the identification of hate speech in code-switched text messages. The alternation of words in different languages within a message is a common occurrence among multilingual persons or communities. The study explored the performance of different features across various machine learning algorithms and established that character-level Term Frequency-Inverse Document Frequency, performed best given a code-switched dataset of 25k annotated tweets using support vector machine algorithm as compared to six other conventional and two deep learning algorithms.