Machine Learning techniques for identifying Cyberbullying on digital networks

Abstract

With the proliferation of virtual entertainment platforms around the world, particularly among young people, digital taunting and enmity have become real and annoying problems that networks must address. Threats can use these levels to attack and weaken others in their networks. To combat digital tormenting, various strategies and tactics have been used or proposed, including early detection and alarms that both detect and protect victims from such attacks. Machine Learning techniques with Artificial Intelligence Framework are being widely used to identify specific linguistic patterns that danger uses to hunt for their victims. The Feeling Analysis (FA) of virtual entertainment content is one of the expanding fields of research in AI. FA makes it possible to gradually identify online harassment and continuously recognize cyberbullying. This study recommends a SA model to identify cyberbullying messages in Facebook web-based entertainment. SVM and MAXENT classifier, controlled AI arrangement tools, are employed in this model. When a higher n-grams language model is applied to such texts in correlation with analogous prior research, the findings of the investigations carried out using this model showed encouraging results. Similar patterns in the results showed that these classifiers preferred execution measures over other classifiers on such remarks.