IPSR Model: Misinformation Intervention through Prebunking in Social Systems

Abstract

The rapid dissemination of misinformation through online social networks poses a growing threat to public understanding and societal stability. Prebunking, a proactive strategy based on inoculation theory, has recently emerged as an effective intervention to build cognitive resilience against misinformation before exposure. In this work, we investigate the impact of prebunking on misinformation dynamics using a compartmental modeling framework. We first analyze the classical Ignorant-Spreader-Stifler (ISR) model, its parameters are determined using empirical rumor data from Twitter. We then propose an extended model, the Ignorant-Prebunked-Spreader-Stifler (IPSR) model, which incorporates prebunking as a preventive state and includes a forgetting mechanism to account for the decay of cognitive immunity over time. Using mean-field approximations, we derive steady-state solutions and examine the effect of prebunking on the spreading of misinformation. We further investigate the robustness of the IPSR model by varying network size and average degree. In addition, we analyze the model's behavior on Watts-Strogatz and Barabasi-Albert networks to assess the role of small-world and scale-free structures in shaping intervention outcomes. Our results show that the inclusion of prebunking significantly reduces the scale of misinformation outbreaks across different network structures. These findings highlight the efficacy of prebunking as a scalable intervention strategy and underscore the utility of compartmental models in understanding and mitigating information-based contagion in complex networks.