Soft Computing Techniques for Detecting Cyberbullying in Social Multimedia Data

Abstract

Cyberbullying is a form of abuse, manipulation, or humiliation directed against a single person via the Internet. CB makes use of nasty Internet comments and remarks. It occurs when someone publicly mocks, insults, slanders, criticizes, or mocks another person while remaining anonymous on the Internet. As a result, there is a rising need to create new methods for sifting through data on social media sites for symptoms of cyberbullying. The goal is to lessen the negative consequences of this condition. This article discusses a soft computing-based methodology for detecting cyberbullying in social multimedia data. This model incorporates social media data. Normalization is performed to remove noise from data. To improve a feature, the Particle Swarm Optimization Technique is applied. Feature optimization helps to make cyberbullying detection more accurate. The LSTM model is used to classify things. With the help of social media data, the PSO LSTM model is getting better at finding cyberbullying. The accuracy of PSO LSTM is 99.1%. It is 2.9% higher than the accuracy of the AdaBoost technique and 10.4% more than the accuracy of the KNN technique. The specificity and sensitivity of PSO-based LSTM is also higher in percentage than KNN and AdaBoost algorithm.