Understanding Contemporary Infodemics through the Risk Amplification through Media Spread (RAMS) Model

Santosh Vijaykumar, Yan Jin, and Aravind Sesagiri Raamkumar (Forthcoming), “Understanding Contemporary Infodemics through the Risk Amplification through Media Spread (RAMS) Model” in Communicating Risk and Safety (Eds., T. Sellnow and D. Sellow). de Gruyter.

Abstract: Infectious disease outbreaks such as the COVID-19 pandemic trigger an avalanche of health information and misinformation on social media – a phenomenon characterized by the World Health Organization (WHO) as an “infodemic”. In this chapter, we explain how the Risk Amplification through Media Spread (RAMS) model clarifies three critical aspects to this phenomenon: (1) the stations or agents who amplify risk, (2) the socially mediated processes through which the infodemic contagion spreads, and (3) the impact of amplification not just of risk but also misinformation on behavioral responses at the societal, community, and individual levels. We describe how artificial intelligence (AI) implementations can use the RAMS model as a basis to develop systems that can leverage the real-time data from social listening tools employed by organizations such as the WHO. In doing so, we inform public health efforts to identify amplification agents and develop rapid communication interventions to create an accurate and equitable information ecosystem that advances public health and safety. Lastly, the chapter suggests alternative public health problem contexts where the RAMS model could be used to identify and address problematic information flows and asymmetries in content.