Detection of Hate Posts and Tweets in the Social Network Society

Abstract

Hate speech is a prevalent issue on social media platforms that has recently been a cause for concern due to its detrimental effects on individuals and society. The development of effective hate speech detection procedures and algorithms is crucial to address this issue. However, the existing natural language processing (NLP) algorithms and machine learning models face several challenges in accurately identifying and categorizing hate speech. These challenges include the ambiguity and variability of language use, the lack of standardized definitions and guidelines for hate speech, and the rapid evolution of new and creative forms of hate speech. In this paper, we propose a technique that leverages classic machine learning and deep learning methods to locate and categorize hate speech in social media. Our approach involves the use of Support Vector Machines (SVM) and Long ShortTerm Memory (LSTM) networks for classification. We evaluate the performance of our model on a hate speech dataset and compare it with a deep learning-based model. Our results show that the SVM model outperforms the deep learning-based model in accuracy and efficiency. Our approach offers a promising solution to the challenges posed by hate speech detection on social media and contributes towards building a safer and more welcoming online community. Keywords—Hate speech, Social Networks, NLP, LSTM, Transformers