A Comprehensive Analysis of Cybercrime Detection on Social Media Using Natural Language Processing Techniques

Abstract

The rapid expansion of social media platforms has revolutionized communication and information sharing, but it has also led to a significant increase in cybercrime activities. These platforms are frequently exploited for illegal activities such as cyberbullying, hate speech, phishing, fraud, identity theft and the spread of malicious content. Detecting such cybercrimes manually is challenging due to the vast volume, velocity and variety of user-generated data. In this context, Natural Language Processing (NLP), a subfield of artificial intelligence, has emerged as a powerful approach for analyzing and detecting cybercrime-related content on social media. This study presents a comprehensive analysis of NLP techniques used for cybercrime detection, focusing on their effectiveness, challenges and real-world applicability. The paper examines traditional NLP methods such as tokenization, part-of-speech tagging and text classification, as well as advanced approaches including machine learning and deep learning models like Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs) and transformer-based architectures such as BERT. It also explores feature extraction methods, sentiment analysis and contextual understanding in identifying malicious intent within textual data. The findings suggest that while NLP-based systems have shown promising results in detecting cybercrime, challenges such as data ambiguity, language diversity, sarcasm and evolving cyber threats still limit their performance. The study concludes by emphasizing the need for more adaptive, scalable and explainable NLP models to effectively combat cybercrime in dynamic social media environments.