AI-Driven War Probability Prediction Using Big Data Analysis of Real and Fake News

Abstract

The increasing complexity of geopolitical conflicts necessitates advanced methods for early detection and prediction of potential wars. This paper proposes a war probability prediction system using Artificial Intelligence (AI) and Big Data analytics to analyze real and fake news content on digital platforms. By employing Natural Language Processing (NLP) and machine learning, the model identifies war-inducing indicators from vast volumes of structured and unstructured data. Historical war information, national profiles, and real-time web content are integrated into a multi-layered AI framework that assigns probabilistic weights based on linguistic cues and contextual relevance. A semantic analysis process ensures the exclusion of inconsistent or misleading content, while national profile variables aid in contextual predictions. The model dynamically learns from historical patterns and emerging trends to offer timely insights. This hybrid system enhances national security strategies by predicting crisis scenarios based on news sentiment and inter-country discourse, ultimately contributing to conflict prevention and strategic decision-making. The approach underscores the transformative potential of AI in safeguarding global peace through intelligent analysis of information warfare.