Presidential communication during the pandemic: a longitudinal examination of its relationship with partisan perceptions and behaviors in the United States.
Partisanship played a key role in shaping individuals' responses to the COVID-19 pandemic in the United States. The current project applies the extended parallel processing model (EPPM) to examine how the content features of White House press conferences were associated with the partisan gap in perc...
| Publicado en: | Human Communication Research Vol. 49; no. 4; pp. 433 - 448 |
|---|---|
| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
Oct2023
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=172331884&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 172331884 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03603989 HUC jtl: Human Communication Research issn: 03603989 maglogo: N pubinfo: dt: Oct2023 vid: 49 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 172331884 10.1093/hcr/hqad025 ppf: 433 ppct: 15 formats: tig: atl: Presidential communication during the pandemic: a longitudinal examination of its relationship with partisan perceptions and behaviors in the United States. aug: au: Li, Yue Wang, Zheng Li, Qin affil: School of Communication, The Ohio State University , Columbus, OH 43210, USA su: United States. Executive Office of the President Partisanship COVID-19 pandemic Public opinion Supervised learning Pandemics Time series analysis sug: subj: Partisanship COVID-19 pandemic Public opinion United States. Executive Office of the President Executive Offices Supervised learning Pandemics Time series analysis keyword: extended parallel processing model pandemic presidential communication supervised machine learning time series extended parallel processing model pandemic presidential communication supervised machine learning time series ab: Partisanship played a key role in shaping individuals' responses to the COVID-19 pandemic in the United States. The current project applies the extended parallel processing model (EPPM) to examine how the content features of White House press conferences were associated with the partisan gap in perceptions and behavior during the early stage of the pandemic. Using supervised machine learning, Study 1 analyzes the White House press conferences regarding the pandemic during 2020. The results demonstrate that the White House focused on efficacy but included minimal threat information. Study 2 uses the threat and efficacy information in White House press conferences to predict perceived threat and efficacy as well as self-quarantine behavior measured by longitudinal surveys using nationally representative samples of U.S. adults. Time-series analysis shows that an increase of threat information from the White House was associated with a subsequent decrease in the partisan gap between Democrats and Republicans on perceived threat and self-quarantine behavior by increasing perceived threat and self-quarantine behavior among Republicans. This study contributes to presidential communication research by systematically examining specific message features and linking them to public perceptions and behaviors in the context of a public health crisis. The study also extends the EPPM to a dynamic model, estimating the asymmetric effects and self-continuity of positive (i.e. efficacy) and negative (i.e. threat) information on perceptions and behaviors. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|