TY - JOUR
T1 - Social Network Analysis of COVID-19 Public Discourse on Twitter
T2 - Implications for Risk Communication
AU - Pascual-Ferrá, Paola
AU - Alperstein, Neil
AU - Barnett, Daniel J.
N1 - Publisher Copyright:
© Society for Disaster Medicine and Public Health, Inc. 2020.
PY - 2020
Y1 - 2020
N2 - ObjectiveThe purpose of this study was to demonstrate the use of social network analysis to understand public discourse on Twitter around the novel coronavirus pandemic (COVID-19). 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.MethodsWe focused on conversations on Twitter during three key communication events from late January to early June of 2020. We used Netlytic, a free web-based software that collects publicly available data from social media sites such as Twitter.ResultsWe 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.ConclusionLooking 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.
AB - ObjectiveThe purpose of this study was to demonstrate the use of social network analysis to understand public discourse on Twitter around the novel coronavirus pandemic (COVID-19). 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.MethodsWe focused on conversations on Twitter during three key communication events from late January to early June of 2020. We used Netlytic, a free web-based software that collects publicly available data from social media sites such as Twitter.ResultsWe 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.ConclusionLooking 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.
KW - COVID-19
KW - World Health Organization (WHO)
KW - risk communication
KW - social media
KW - social network analysis
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U2 - 10.1017/dmp.2020.347
DO - 10.1017/dmp.2020.347
M3 - Article
C2 - 32907685
AN - SCOPUS:85092533693
JO - Disaster Medicine and Public Health Preparedness
JF - Disaster Medicine and Public Health Preparedness
SN - 1935-7893
ER -