An Applied Artificial Intelligence Model for Social Media-Based Natural Crisis Management in Iran

Abstract

Natural hazards in Iran frequently generate complex communication demands because earthquakes, floods, droughts, and other disasters unfold in environments where social media platforms rapidly become both information resources and rumor amplifiers. This study developed an applied model for the use of artificial intelligence (AI) in social media for natural crisis management in Iran, with particular attention to detecting, analyzing, and countering misinformation. The article is based on the findings of a mixed-method, exploratory-sequential study conducted in five consecutive phases. First, semi-structured interviews with 16 experts in crisis management, media, artificial intelligence, and relief operations were analyzed thematically, producing nine major themes and ten key factors; instrument validity was confirmed through CVR and CVI, and reliability was supported by Cohen's kappa of 0.847. Second, interpretive structural modeling (ISM) with an eight-member expert panel identified an integrated crisis data monitoring center and transparent privacy legislation as foundational drivers, while reducing response time emerged as the ultimate system outcome. Third, a quantitative analysis of 384 social media messages from the Varzaghan-Ahar earthquake, the Khoy earthquake, and the Lorestan flood showed that 16.9% of messages contained misinformation and more than 45% of rumors appeared during the first six hours of crisis. Fourth, six AI algorithms were evaluated; ParsBERT performed best for Persian text with 88.2% test-set accuracy and an F1 score of 85.7%, CNN achieved 90.0% accuracy in the reported image test subset, and RNN-LSTM reached 83.3% accuracy in the reported video test subset. Finally, the findings were integrated into a five-layer operational model consisting of data, preprocessing, intelligent analysis, human verification and feedback, and decision/action layers. The model specifies three major objectives: preventing rumors and misinformation, reducing response time, and strengthening community resilience. The findings indicate that AI-supported social media monitoring can improve crisis communication, but implementation requires technical, ethical, legal, and social safeguards.