Application of LSTM and GloVe Word Embedding for Hate Speech Detection in Indonesian Twitter Data

Abstract

Hate speech, characterized by intentional expressions of dissatisfaction, is a prevalent phenomenon on social media platforms, including Twitter. Its continual occurrence can foster divisions, misunderstandings, and even acts of violence between individuals and groups, particularly due to the resulting prejudice. This study investigates the occurrence of hate speech within Indonesian content on Twitter, employing a deep learning approach to detect and analyze such expressions. The Long Short-Term Memory (LSTM) method, coupled with the GloVe word embedding technique, is utilized on a dataset comprising 13,169 Indonesian tweets flagged for hate speech. Four distinct model architectures were developed through the integration of LSTM and GloVe. The findings reveal model 1 to exhibit superior performance, achieving a precision of 89%, a recall of 99%, an F-1 score of 94%, and an overall accuracy of 94.24%. It is suggested that future research explore the potential deployment of this model in web or mobile platforms for real-time analysis, thereby enhancing the capacity for immediate hate speech detection and mitigation.