Predictive Analytics through AI: Identifying Social Media Threat Patterns

Abstract

The proliferation of social media platforms has introduced new challenges in monitoring and identifying potential threats, ranging from cyber-attacks to misinformation campaigns. This study explores using Artificial Intelligence (AI) for predictive analytics in identifying patterns associated with social media threats. Leveraging machine learning algorithms and Natural Language Processing (NLP), we developed an AI model capable of analyzing massive volumes of social media data to detect threat patterns and anticipate potential security risks. Our approach integrates sentiment analysis, keyword extraction, and network mapping to build predictive models that capture the complex dynamics of social media interactions. The proposed system was evaluated on a dataset collected from major social platforms over six months, focusing on detecting coordinated threats and abnormal user behaviors. Experimental results demonstrate that our AI model achieved an accuracy of 92% in identifying precursors to security threats, outperforming baseline methods by approximately 15%.
Furthermore, the model provides a scalable solution for real-time monitoring, making it feasible for deployment in various applications, from law enforcement to corporate security. The findings indicate that AI-driven predictive analytics can be a valuable tool for proactively addressing threats on social media, contributing to safer digital environments. Future research will enhance model robustness across different languages and social media platforms, improving cross-platform threat detection.