Social Media Fake News Detection in War Zones: Allied to AI Smooth Detector

Abstract

Fake news and disinformation have emerged as potent tools of modern information warfare, shaping perceptions and influencing outcomes in war zones. Unlike traditional cyber threats that target data or networks, fake news manipulates the human layer of security, exploiting emotions, trust, and social vulnerabilities. This paper investigates SmoothDetector, a probabilistic multimodal AI framework, for detecting fake news on social media during conflict. The system integrates textual analysis, image forensics, contextual credibility scoring, and probabilistic fusion to provide real-time classification and alerts. Experiments on benchmark datasets (FakeNewsNet, LIAR, Twitter15/16) and simulated war-zone streams demonstrate detection accuracies above 90%, resilience to compression and multilingual noise, and real-time detection with latency under 2–7 seconds depending on hardware. The results position SmoothDetector as a viable tool for deployment in command centers, newsrooms, humanitarian agencies, war victims and military information operations. Limitations such as adversarial evasion, false positives, and ethical risks are also discussed, along with a deployment playbook for conflict environments.