Leveraging Artificial Intelligence and Machine Learning in Online Threat Detection - Shamroukh, S; Johnson, T (International Journal of Computer Sciences and Engineering, 2025)
This literature review examines the roles of artificial intelligence (AI), machine learning (ML), and large language models (LLMs) in identifying and interpreting online threats. As AI and ML technologies advance, their use in analyzing vast online data for potential threats has grown significantly. The review systematically evaluates current methodologies for detecting and assessing threats, particularly in social media and online forums, which are both information hubs and sources of harmful content. Key findings highlight the effectiveness of BERT-based models in hate speech detection across languages and platforms, emphasizing their interpretability and transparency advantages over traditional neural networks. Models like GPT-4 further expand threat identification capabilities, detecting cyber threats and abusive language, with implications for public safety and mental health monitoring. Challenges remain, particularly in handling noisy, diverse, and imbalanced social media datasets. Domain-specific word embeddings and ensemble techniques, such as combining BERT with TextCNN and BiLSTM, show promise in improving detection accuracy in complex data environments. The review advocates for continued focus on hybrid and ensemble models to address data complexities and calls for future research to enhance model transparency and address ethical concerns like data privacy. Given the rise in digital communication, real-time threat detection is crucial for public safety, national security, and violence prevention. This review consolidates findings on the efficacy of AI and ML in detecting online threats, identifies recurring challenges, and outlines research gaps to guide future advancements. By synthesizing recent studies, it provides a structured analysis of the current capabilities and limitations of AI and ML in online threat monitoring, contributing to a foundational understanding of how these technologies can evolve to enhance societal safety.