CivilityCheck: An Integrated Natural Language Processing and Machine Learning Framework to Detect Hateful and Offensive Language

The proliferation of hate speech and offensive language in online communities has escalated into a significant societal concern, prompting an urgent demand for robust tools capable of automatically identifying and addressing such content. Introducing CivilityCheck, a comprehensive framework that seamlessly integrates Natural Language Processing (NLP) and Machine Learning (ML) methodologies to uncover instances of hate speech and offensiveness within online discourse. Utilizing logistic regression models trained on Twitter datasets, CivilityCheck demonstrates remarkable efficacy, achieving a notable test accuracy of 95.4%. Moreover, the proposed framework extends its functionality into a user-friendly web browser extension, facilitating real-time analysis of social media content to cultivate a safer online environment. By endowing users with the capability to promptly identify and address toxic language, CivilityCheck contributes significantly to the propagation of civility and respect in digital interactions. This innovative solution stands at the forefront of combating online toxicity, offering a potent means to foster constructive dialogue and mitigate the harmful effects of coarse and regressive language usage on digital platforms.