One Earth, One Humanity Versus the Virus: Examining the Global COVID-19 Social Partnership Communication Networks on Social Media

The COVID-19 pandemic has posed severe challenges that require collaborative efforts from multi-sector organizations. Guided by an institutional theory framework that considers how both organizational fields and national level contexts affect organizations' social partnership communication, the current study examines the COVID-19-related social partnership communication network on social media. The cross-national study using semantic network analysis and exponential random graph models (ERGMs) first maps the meaning of COVID-19 social partnership network, and then investigates the role of organizational fields and a country's political system, economic system, educational system, and cultural system on the formation of interorganizational communication ties surrounding the relief efforts of COVID-19. Results reveal the importance of the political system—such as the presence of populist government, economic disparity, and uncertainty avoidance cultural orientation in shaping the social media-based social partnership communication network. In addition, NGOs from multiple issue areas are actively engaged in the network, whereas corporations from manufacturing and financial industries are active players.

Policy Communication In Times of Public Health Crisis: Longitudinal Network Modeling of U.S. Politician-Health Agency Interactions During the COVID-19 Pandemic (preprint)

Severe public health crisis entails coordinated communication between politicians and public health agencies. The study explores how U.S. politicians share messages from health agencies on social media during COVID-19. Proposing a multi-theoretical, multi-level (MTML) framework, we draw upon the Advocacy Coalition Framework and Crisis and Emergency Risk Communication theory and conceptualize politicians’ public health communication as serving the dual functions of policy and risk communication. With bipartite longitudinal network modeling, our analysis finds a fragmented national message-sharing network without central federal leadership and clustered around state-level actors such as local health agencies and state governors. The politicians’ party affiliation and positions on COVID-19 policies significantly impacted whether they would distribute messages from public health agencies. Health agencies’ message features such as expression of certainty and use of analytical words also influenced politicians’ sharing patterns. These findings reveal novel theoretical and practical insights by integrating two theories under the MTML framework.