Detecting Hate Speech Utilizing The Vendi Score: An Emerging Hate Speech Detection Algorithm

Abstract

This research paper explores the development and application of hate speech detection algorithms within the social media platform of Twitter, but containing the possibility of being utilized for the purposes of social media platforms like Instagram. With the rapidly increasing usage of online communication, social media sites have become fertile grounds for the dissemination of hate speech, which pose a significant challenge to maintaining a safe, respectful, and inclusive online, social environment. This study examines the current state of hate speech detection technologies, including machine learning and natural language processing techniques, to understand their effectiveness and limitations. I also delve into the ethical considerations and potential biases inherent in these algorithms, as well as their impact on free speech. Throughout these considerations, I helped develop an algorithm to give weights to a dataset in an effort to detect hate speech found within in Twitter tweets by adapting Vertaix's Vendi Score algorithm. Through a comprehensive review of literature and case studies, this paper aims to provide insights into how these technologies are implemented, the challenges they face in accurately identifying and mitigating hate speech, an implementation of an algorithm to attempt to solve the issue, and future directions for improving the efficacy and fairness of hate speech detection systems.