Political Hate Speech Detection using Machine Learning

Abstract

In the digital age, the detection of hate speech has become a crucial task, given the pervasive and detrimental impact of offensive content on individuals and communities. This project is dedicated to developing a machine learning-driven approach for the identification and mitigation of hate speech within online platforms.The primary aim of this research is to explore the effectiveness of various machine learning algorithms in the accurate and efficient detection of hate speech in political speeches. The project entails the collection and preprocessing of a substantial dataset consisting of user- generated content from social media platforms and online forums, with annotations indicating hate speech content. The dataset is then partitioned into training, validation, and testing sets, and natural language processing techniques are employed to extract relevant features. Diverse machine learning models, including logistic regression, support vector machines are implemented and fine-tuned using the training data. In summary, this contributes to the ongoing fight against hate speech in political speeches by harnessing the power of machine learning. The findings not only shed light on the challenges and potential solutions in hate speech detection but also pave the way for the development of more robust and precise systems, ultimately fostering a safer and more inclusive online environment. Key Words: Hate Speech Detection, Political Hate Speech , Machine Learning, Offensive Content , Support Vector Machine.