Securing Social Spaces: Cyberbullying Detection with ML and DL on Social Media Platforms

Genuine dialogue is encouraged in safe social environments. Conversations become more meaningful and real when people feel free to be themselves, voice their perspectives, and share their experiences without filtering or censoring their ideas. With the growth of social media and online communication, cyberbullying on these platforms has become a serious and worrying problem. Cyberbullying is the term for the practice of harassing, intimidating, threatening, or harming people or groups through digital platforms like social media, messaging applications, or other online platforms. Social media posts may include text, photographs, and videos that can be analyzed by artificial intelligence (AI) systems to find potentially offensive or hazardous information. NLP (natural language processing) models can recognize threats, hate speech, and other unpleasant communications. This article provides a comprehensive examination of the pattern identification capabilities of machine learning and deep learning methodologies in the context of identifying instances of cyberbullying on social media platforms at an early stage. Moreover, this study presents a comparative analysis of machine learning and deep learning methodologies in the context of cyberbullying tweets collected from the online social media community. The obtained outcomes are thoroughly examined to demonstrate the importance of each method, as measured by metrics like as test accuracy, train accuracy, precision, and recall.