Lu Tang1, Wenlin Liu2, Benjamin Thomas3, Hong Thoai Nga Tran4, Wenxue Zou1, Xueying Zhang5, Degui Zhi4. 1. Department of Communication, Texas A&M University, Texas A&M University, College Station, US. 2. Jack J. Valenti School of Communication, University of Houston, Houston, US. 3. Department of Computer Science, Rice University, Houston, US. 4. School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, US. 5. Department of Journalism and Mass Communication, North Carolina A&T State University, Greensboro, US.
Abstract
BACKGROUND: The ongoing COVID-19 pandemic is characterized by different morbidity and mortality rates across different states, cities, rural areas, and diverse neighborhoods. The absence of a national strategy in battling the pandemic also leaves state and local governments responsible for creating their own response strategies and policies. OBJECTIVE: This study examines the content of the tweets sent by public health agencies in Texas about COVID-19 and how content characteristics predict the level of public engagement. METHODS: All COVID-19 related tweets (n=7269) posted by Texas public agencies during the first six months of 2020 were classified in terms of each tweet's functions (whether the tweet provides information, promotes action, or builds community), preventative measures mentioned, and health beliefs discussed using natural language processing. Hierarchical linear regressions were run to explore how tweet content predicted public engagement. RESULTS: Information was the most prominent function, followed by action and community. Susceptibility, severity, and benefits were the most frequently covered health beliefs. Tweets serving the information or action functions were more likely to be retweeted, while tweets performing the action and community functions were more likely to be liked. Tweets communicating susceptibility information led to most public engagement in terms of both retweeting and liking. CONCLUSIONS: Public health agencies should continue to use Twitter to disseminate information, promote action, and build communities. They need to improve social media message strategies regarding the benefit of disease prevention behaviors and audiences' self-efficacy.
BACKGROUND: The ongoing COVID-19 pandemic is characterized by different morbidity and mortality rates across different states, cities, rural areas, and diverse neighborhoods. The absence of a national strategy in battling the pandemic also leaves state and local governments responsible for creating their own response strategies and policies. OBJECTIVE: This study examines the content of the tweets sent by public health agencies in Texas about COVID-19 and how content characteristics predict the level of public engagement. METHODS: All COVID-19 related tweets (n=7269) posted by Texas public agencies during the first six months of 2020 were classified in terms of each tweet's functions (whether the tweet provides information, promotes action, or builds community), preventative measures mentioned, and health beliefs discussed using natural language processing. Hierarchical linear regressions were run to explore how tweet content predicted public engagement. RESULTS: Information was the most prominent function, followed by action and community. Susceptibility, severity, and benefits were the most frequently covered health beliefs. Tweets serving the information or action functions were more likely to be retweeted, while tweets performing the action and community functions were more likely to be liked. Tweets communicating susceptibility information led to most public engagement in terms of both retweeting and liking. CONCLUSIONS: Public health agencies should continue to use Twitter to disseminate information, promote action, and build communities. They need to improve social media message strategies regarding the benefit of disease prevention behaviors and audiences' self-efficacy.
Authors: Elisabetta Ceretti; Loredana Covolo; Francesca Cappellini; Alberto Nanni; Sara Sorosina; Andrea Beatini; Mirella Taranto; Arianna Gasparini; Paola De Castro; Silvio Brusaferro; Umberto Gelatti Journal: J Med Internet Res Date: 2022-09-13 Impact factor: 7.076