Social Network Analysis of COVID-19 Public Discourse on Twitter: Implications for Risk Communication

Abstract

Objectives: The purpose of this study was to demonstrate the use of social network analysis to understand public discourse on Twitter around the novel coronavirus disease 2019 (COVID-19) pandemic. We examined different network properties that might affect the successful dissemination by and adoption of public health messages from public health officials and health agencies. Methods: We focused on conversations on Twitter during 3 key communication events from late January to early June of 2020. We used Netlytic, a Web-based software that collects publicly available data from social media sites such as Twitter. Results: We found that the network of conversations around COVID-19 is highly decentralized, fragmented, and loosely connected; these characteristics can hinder the successful dissemination of public health messages in a network. Competing conversations and misinformation can hamper risk communication efforts in a way that imperil public health. Conclusions: Looking at basic metrics might create a misleading picture of the effectiveness of risk communication efforts on social media if not analyzed within the context of the larger network. Social network analysis of conversations on social media should be an integral part of how public health officials and agencies plan, monitor, and evaluate risk communication efforts.