Detection of Cyberbullying Across Social Networks

In the era of social media and networking, hostile language and profanity have increased dramatically. Youth contribute substantially to it. More than fifty percent of young people who engage with social media are victims of cyberbullying. Intimidating remarks on social media platforms result in negative repercussions within the network. These remarks contribute to an online culture of disregard. At this time, the majority of algorithms and techniques used to comprehend and reduce it are inactive. Presently, the recall rates of insult detection applications that employ machine learning and natural language processing are exceptionally low. The main objective is to detect instances of bullying within a text through the implementation of diverse categorization methods for remarks. Following the development of an effective algorithm to identify abuse and aggressive remarks, we evaluated the algorithm's precision. By employing NLP and machine learning, the antagonistic impact of an individual or group can be identified through the analysis of social comments. A critical component of a fully developed prototype system intended to detect cyberbullying on social media is an effective classifier.