Transforming Global Crisis Communication Through Digital Twins Enhancing Media Response Strategies With Machine Learning

Abstract

In an increasingly interconnected and crisis-prone world, effective communication during emergencies remains a critical challenge. This study systematically explores the transformative role of digital twins and machine learning (ML) in enhancing crisis communication strategies across multiple domains, including pandemics, natural disasters, cybersecurity incidents, and social media engagement. A total of 259 peer-reviewed articles were analyzed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a rigorous and transparent review process. The findings highlight that digital twins provide real-time, high-fidelity simulations of crisis dynamics, enabling decision-makers to anticipate challenges, allocate resources efficiently, and optimize emergency responses. Concurrently, machine learning techniques such as deep learning, predictive analytics, and natural language processing (NLP) facilitate misinformation detection, sentiment analysis, and the prediction of public emotional responses. The integration of digital twins and machine learning demonstrates significant advancements in crisis management by offering a data-driven framework for improving situational awareness, reducing response time, and enhancing communication transparency. In pandemic scenarios, digital twins optimize healthcare logistics, while machine learning mitigates misinformation. In natural disasters, these technologies enable dynamic resource allocation and adaptive communication. Similarly, in cybersecurity crises, digital twins simulate attack scenarios, and ML tools detect threats in real time. Moreover, social media analysis through ML identifies public sentiment trends and key influencers to ensure effective public engagement.