Implementation of Political Hate Speech Detection using Machine Learning

Implementation of Political Hate Speech Detection using Machine Learning

A.    H. Kagne*1, Aditya Sanjay Khot*2, Deepraj Bharat Patil*3, Harshwardhan Babasaheb Darade*4, Rohit Baban Chavhan*5.

. 1Professor, Department of Computer Engineering, Sinhgad Academy Of Engineering, Kondhwa, Pune, Maharashtra, India.

2,3,4,5Student, Computer Engineering, Sinhgad Academy of Engineering, Kondhwa, Pune, Maharashtra, India.

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ABSTRACT

Political hate speech detection is a critical field of study aiming to uphold civil discourse, social harmony, and democratic values in the digital age. This project focuses on developing an advanced machine learning hate speech detection system, employing the Support Vector Machine (SVM) algorithm. Diverse text data from social media, news sites, and forums are collected and meticulously annotated for accuracy by experts. The system, built using the Spyder IDE, offers features such as an advanced editor, interactive consoles, and a documentation viewer. Following the Software Development Life Cycle (SDLC) waterfall model, the project progresses through requirements analysis, design, implementation, testing, deployment, and maintenance.

The SVM algorithm is the cornerstone, effectively categorizing text data into political hate speech or non-hate speech. Performance metrics such as precision, recall, and accuracy are assessed to ensure the model's efficacy. The project emphasizes a balance between technological innovation and ethical considerations, aiming to enhance healthy online political discourse.

By systematically addressing the challenges of hate speech detection, this project underscores the importance of human oversight, ethical guidelines, and ongoing discussions on free speech and harm prevention in the digital landscape. Ultimately, it seeks to contribute to fostering inclusivity and respect in digital political communication spaces.

This project focuses on developing a cutting-edge machine learning system for political hate speech detection, leveraging the Support Vector Machine (SVM) algorithm. It collects diverse text data from various online sources, ensuring accurate annotation by experts. The system, built with the Spyder IDE, provides a range of features such as an advanced editor and interactive consoles. Following the SDLC waterfall model, the project advances through key stages from requirements analysis to deployment. The SVM algorithm plays a central role, categorizing text into political hate speech or non-hate speech categories, with performance metrics evaluated for effectiveness. The project emphasizes the importance of balancing technological innovation with ethical considerations for healthy online political discourse. Through this systematic approach, it aims to contribute to fostering inclusivity and respect in digital political communication spaces.

Key Words:  Hate Speech Detection, Political Hate Speech , Machine Learning, Offensive Content , Support Vector Machine.