Hate Speech on Social Platforms Through the Application of ML and NLP Methods

Hateful behavior on social platforms has recently become a topic of interest for many researchers. Users experience online encounters with instances of hate speech on a daily basis. This paper investigates how using modern machine learning and natural language processing techniques and methods make computer systems enhance their intelligence to effectively recognize words indicative of hate speech or insults. A performance comparison is conducted using an extensive dataset of publicly available posts, evaluating traditional classifiers against classifiers that rely on deep learning. The results indicate that the overall success of the model is not solely determined by the choice of classifier, but also by factors such as pre-processing of textual data and the accurate configuration of parameters.