Texas Public Agencies’ Tweets and Public Engagement During the COVID-19 Pandemic: Natural Language Processing Approach

Simple item page
dc.citation.articleNumbere26720en_US
dc.citation.issueNumber4en_US
dc.citation.journalTitleJMIR Public Health Surveillanceen_US
dc.citation.volumeNumber7en_US
dc.contributor.authorTang, Luen_US
dc.contributor.authorLiu, Wenlinen_US
dc.contributor.authorThomas, Benjaminen_US
dc.contributor.authorTran, Hong Thoai Ngaen_US
dc.contributor.authorZou, Wenxueen_US
dc.contributor.authorZhang, Xueyingen_US
dc.contributor.authorZhi, Deguien_US
dc.date.accessioned2021-05-21T18:50:15Zen_US
dc.date.available2021-05-21T18:50:15Zen_US
dc.date.issued2021en_US
dc.description.abstractBackground: 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 for 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 COVID-19–related tweets posted by public health agencies in Texas and how content characteristics can predict the level of public engagement. Methods: All COVID-19–related tweets (N=7269) posted by Texas public agencies during the first 6 months of 2020 were classified in terms of each tweet’s functions (whether the tweet provides information, promotes action, or builds community), the preventative measures mentioned, and the health beliefs discussed, by using natural language processing. Hierarchical linear regressions were conducted to explore how tweet content predicted public engagement. Results: The information function was the most prominent function, followed by the action or community functions. Beliefs regarding susceptibility, severity, and benefits were the most frequently covered health beliefs. Tweets that served the information or action functions were more likely to be retweeted, while tweets that served the action and community functions were more likely to be liked. Tweets that provided susceptibility information resulted in the most public engagement in terms of the number of retweets and likes. Conclusions: Public health agencies should continue to use Twitter to disseminate information, promote action, and build communities. They need to improve their strategies for designing social media messages about the benefits of disease prevention behaviors and audiences’ self-efficacy.en_US
dc.identifier.citationTang, Lu, Liu, Wenlin, Thomas, Benjamin, et al.. "Texas Public Agencies’ Tweets and Public Engagement During the COVID-19 Pandemic: Natural Language Processing Approach." <i>JMIR Public Health Surveillance,</i> 7, no. 4 (2021) JMIR: https://doi.org/10.2196/26720.en_US
dc.identifier.doihttps://doi.org/10.2196/26720en_US
dc.identifier.urihttps://hdl.handle.net/1911/110632en_US
dc.language.isoengen_US
dc.publisherJMIRen_US
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleTexas Public Agencies’ Tweets and Public Engagement During the COVID-19 Pandemic: Natural Language Processing Approachen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Tweets-Covid.pdf
Size:
308.59 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Rice Coronavirus Research