Evolutionary Algorithm with Graph Neural Network Driven Cyberbullying Detection on Low Resource Asian Languages

Abstract

ICT is widely adopted by Asian youth and is utilized by people of all ages across the continent. Despite its many advantages, unethical ICT usage can lead to many complications. A harmful application of ICT for social communication and engagement is cyberbullying. Simply adhering to the generally accepted norms and guidelines for cybersecurity will not protect you from cybercrime. Even well-known social media stages like Twitter are safe from this attack. Natural language processing (NLP) research on cyberbullying detection has become popular recently. Even though old-style NLP procedures have become highly cyberbullying, there are still hurdles to overcome. These include the limited character count allowed by social media platforms, an imbalance among comments, ambiguity, and unnecessary use of slang. Models based on (CNNs), Multilayer Perceptrons (MLPs), and (RNNs), have recently shown encouraging results in a variety of NLP tasks. With this motivation, this research develops an African vulture optimization algorithm with a graph neural network-based cyberbullying detection and classification (AVOAGNN-CBDC) model. The proposed AVOAGNN-CBDC technique mainly intends to detect and classify cyberbullying. The AVOAGNN-CBDC technique undergoes data preprocessing in different stages and a FastText-based word embedding process to achieve this. Besides, the AVOAGNN-CBDC technique employs the GNN model for cyberbullying detection and classification. Finally, the AVOA is used for the optimal parameter selection of the GNN model, which helps achieve improved classification performance. The experimental result investigation of the AVOAGNN-CBDC technique is tested on the cyberbullying dataset, and the outcomes highlighted the supremacy of the AVOAGNN-CBDC technique in terms of several measures.