Digital Twin Frameworks for Predictive Social Behavior Combining IoT Big Data and AI to Analyze and Shape Community Engagement

Abstract

: Digital twin frameworks have the potential to transform the integration of IoT, big data and AI and benefit challenges related to the modernization of community participation and social behavior analysis. Although manufacturing and industrial domains have seen significant attention in the literature, there is still ample space to bridge the utilization of digital twin technology to predictive social behavior and at the societal level. In doing so, this study fills these gaps by proposing an interdisciplinary framework to leverage IoT based real time data, next-generation big data analytics, and AI-powered predictive modeling. This framework provides actionable insights into patterns of community engagement, leads to proactive interventions, and ultimately better societal outcomes. To illustrate, this study showcases how digital twins can impact public participation, enhance policy responsiveness, and contribute to smarter, more inclusive living through practical validation and case studies. By placing a greater emphasis on ethical framing, scalability, and adaptability, this approach fills significant gaps in existing literature and establishes a model for future predictive digital twin systems in social contexts.