Advancing Ethical and Accurate Hate Speech Detection with Machine Learning Techniques
Authors:-Jorge White
Abstract- In recent years, the proliferation of social media platforms has significantly increased, providing a digital space where individuals from diverse backgrounds can express their opinions and thoughts. This surge in social media usage has brought to light the challenge of managing and moderating hate speech—a form of content that can incite violence, discrimination, and hostility. The primary difficulties in detecting and processing hate speech stem from the linguistic diversity of users, the nuanced usage of language that can alter meanings based on context, and the scarcity of robust datasets for the development and evaluation of detection models. This paper explores these challenges in depth and proposes an innovative approach to enhance the efficiency and effectiveness of hate speech detection. We critically analyze the limitations inherent in current methodologies and introduce a model based on Support Vector Machine (SVM) algorithms. Our comparative analysis demonstrates that SVM-based models offer superior performance in detecting hate speech compared to conventional neural network approaches. This is attributed to the SVM’s ability to handle high-dimensional data and its effectiveness in classifying complex, nuanced linguistic patterns. Furthermore, we delve into the technical and ethical implications of automating hate speech detection. The paper discusses the ongoing challenges in balancing accuracy with the need for ethical considerations, such as avoiding censorship and respecting free speech. We address the technical hurdles related to algorithmic bi as, model interpretability, and the need for continuous adaptation to evolving language and social norms. In conclusion, while significant strides have been made in employing machine learning techniques for hate speech detection, several critical issues remain unresolved. Our research underscores the importance of interdisciplinary efforts, combining insights from linguistics, social sciences, and computer science, to develop more sophisticated, ethical, and effective hate speech detection systems. By advancing the use of SVM and exploring its potential in this domain, we contribute to the broader discourse on making digital platforms safer and more inclusive.
