Detecting Cybercrime: An Evaluation of Machine Learning and Deep Learning Using Natural Language Processing Techniques on the Social Network

Abstract

The widespread use of online social networks has culminated in across-the-board social communication among users, resulting in a considerable amount of user-generated contact data. Cybercrime has become a significant issue in recent years with the rise of online communication and social network. Cybercrime has lately been identified as a severe national psychological concern among platform users, and building a reliable detection model is crucial. Cyberbullying is the phrase used to describe such online harassment, insults, and attacks. It has become challenging to identify such unauthorized content due to the massive number of user-generated content. Because deep neural networks have various advantages over conventional machine learning approaches, researchers are turning to them more frequently to identify cyberbullying. Deep learning and machine learning have several uses in text classification. This article suggested the novel neural network model through parameters of an algorithmic and optimization comparative analysis of nine category approaches, four neural networks, and five machine learning, in two scenarios with real-world datasets of cyberbullying. Moreover, this work also analyzes the impact of word embedding and feature extraction techniques based on text mining and NLP on algorithms' performances. We performed extensive experiments on the two scenarios with a split dataset to demonstrate the merit of this research, comparing nine classification approaches through five feature extraction techniques. Our proposed cybercriminal detection model using neural networks, deep learning, and machine learning outperforms the existing state-of-the-art method of cybercriminal detection in terms of accuracy achieving higher performance.