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Author | Shangde Gao; Yan Wang; Lisa Platt | ||||
Title | Modeling U.S. Health Agencies' Message Dissemination on Twitter and Users' Exposure to Vaccine-related Misinformation Using System Dynamics | Type | Conference Article | ||
Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
Volume | Issue | Pages | 333-344 | ||
Keywords | COVID-19, misinformation, social media, System Dynamics, vaccine hesitancy | ||||
Abstract | This research intends to answer: how do (i) generation frequency and (ii) retweeting count of health agencies' messages impact the exposure of the general users to vaccine-related misinformation on Twitter? We creatively employed a Susceptible-Infected-Recovered (SIR) System Dynamics paradigm to model interactions between message dissemination of 168 U.S. health agencies and proportions of users who are at different exposure statuses to misinformation, namely “Susceptible”, “Infected”, or “Recovered” status. The SIR model was built based on the vaccine-relevant tweets posted over November and December in 2020. Our preliminary outcomes suggest that augmenting the generation frequency of agencies' messages and increasing retweeting count can effectively moderate the exposure risk to vaccine-related misinformation. This model illustrates how health agencies may combat vaccine hesitancy through credible information dissemination on social media. It offers a novel approach for crisis informatics studies to model different information categories and the impacted population in the complex digital world. | ||||
Address | University of Florida; University of Florida; University of Florida | ||||
Corporate Author | Thesis | ||||
Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
Language | English | Summary Language | English | Original Title | |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
Track | Disaster Public Health & Healthcare Informatics in the Pandemic | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
Notes | gao.shangde@ufl.edu | Approved | no | ||
Call Number | ISCRAM @ idladmin @ | Serial | 2337 | ||
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