Purging the Poison: A Machine Learning Approach to Filtering Toxic Comments

Abstract

The rapid growth of online communication platforms has provided unprecedented opportunities for global dialogue. Yet, it has also introduced challenges such as the proliferation of toxic comments, which can have severe consequences for individuals and communities. This research paper proposes a machine learning-based approach to mitigate the impact of toxic comments by automatically identifying and filtering them from online discussions. Our study begins by curating a comprehensive dataset of labeled comments, encompassing a spectrum of toxicity levels. Leveraging state-of-the-art natural language processing techniques, we extract relevant features from the textual content, including sentiment, context, and linguistic patterns. These features serve as inputs to a machine learning model, trained on a diverse range of toxic and non-toxic comments. In conclusion, this research contributes to the development of intelligent content moderation systems that foster healthier online discourse. By implementing machine learning algorithms, we aim to provide a scalable and effective solution for identifying and filtering toxic comments, ultimately promoting a more inclusive and respectful online environment.