Cyberbullying detection using ai and ml

The importance of online social networking has increased a part of our daily lives as it makes it easier to engage with others. However, detecting cyberbullying is an important topic to be examined due to the emergence of antisocial behavior faced by social media users due to issues such as hate speech, trolling, and cyberbullying on platforms such as Twitter and other social media experiences, which undoubtedly affects the psyche of victims. Social networks have become within the reach of everyone, especially children, and when children are exposed to electronic bullying, this affects the building of their personality and psyche. In this paper, reliable methods for detecting cyberbullying are presented. It shows how deep learning works and its high accuracy for detecting cyberbullying, and achieved the highest result, (0.96), by anticipating bad words, which helps reduce the spread of bullying in the means of communication, and as we mentioned another method, which is SVM, the result was good, which is (0.92). , but it is considered less than Deep learning, which affects the discovery of cyberbullying, and we also mentioned another method, which is KNN, which was the lowest result, as it achieved (0.90). The results show us the importance of this research and reliable methods to reduce the spread of cyberbullying.

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