Cargando…
Partisan Differences in Twitter Language Among US Legislators During the COVID-19 Pandemic: Cross-sectional Study
BACKGROUND: As policy makers continue to shape the national and local responses to the COVID-19 pandemic, the information they choose to share and how they frame their content provide key insights into the public and health care systems. OBJECTIVE: We examined the language used by the members of the...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8176946/ https://www.ncbi.nlm.nih.gov/pubmed/33939620 http://dx.doi.org/10.2196/27300 |
_version_ | 1783703333852151808 |
---|---|
author | Guntuku, Sharath Chandra Purtle, Jonathan Meisel, Zachary F Merchant, Raina M Agarwal, Anish |
author_facet | Guntuku, Sharath Chandra Purtle, Jonathan Meisel, Zachary F Merchant, Raina M Agarwal, Anish |
author_sort | Guntuku, Sharath Chandra |
collection | PubMed |
description | BACKGROUND: As policy makers continue to shape the national and local responses to the COVID-19 pandemic, the information they choose to share and how they frame their content provide key insights into the public and health care systems. OBJECTIVE: We examined the language used by the members of the US House and Senate during the first 10 months of the COVID-19 pandemic and measured content and sentiment based on the tweets that they shared. METHODS: We used Quorum (Quorum Analytics Inc) to access more than 300,000 tweets posted by US legislators from January 1 to October 10, 2020. We used differential language analyses to compare the content and sentiment of tweets posted by legislators based on their party affiliation. RESULTS: We found that health care–related themes in Democratic legislators’ tweets focused on racial disparities in care (odds ratio [OR] 2.24, 95% CI 2.22-2.27; P<.001), health care and insurance (OR 1.74, 95% CI 1.7-1.77; P<.001), COVID-19 testing (OR 1.15, 95% CI 1.12-1.19; P<.001), and public health guidelines (OR 1.25, 95% CI 1.22-1.29; P<.001). The dominant themes in the Republican legislators’ discourse included vaccine development (OR 1.51, 95% CI 1.47-1.55; P<.001) and hospital resources and equipment (OR 1.22, 95% CI 1.18-1.25). Nonhealth care–related topics associated with a Democratic affiliation included protections for essential workers (OR 1.55, 95% CI 1.52-1.59), the 2020 election and voting (OR 1.31, 95% CI 1.27-1.35), unemployment and housing (OR 1.27, 95% CI 1.24-1.31), crime and racism (OR 1.22, 95% CI 1.18-1.26), public town halls (OR 1.2, 95% CI 1.16-1.23), the Trump Administration (OR 1.22, 95% CI 1.19-1.26), immigration (OR 1.16, 95% CI 1.12-1.19), and the loss of life (OR 1.38, 95% CI 1.35-1.42). The themes associated with the Republican affiliation included China (OR 1.89, 95% CI 1.85-1.92), small business assistance (OR 1.27, 95% CI 1.23-1.3), congressional relief bills (OR 1.23, 95% CI 1.2-1.27), press briefings (OR 1.22, 95% CI 1.19-1.26), and economic recovery (OR 1.2, 95% CI 1.16-1.23). CONCLUSIONS: Divergent language use on social media corresponds to the partisan divide in the first several months of the course of the COVID-19 public health crisis. |
format | Online Article Text |
id | pubmed-8176946 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-81769462021-06-22 Partisan Differences in Twitter Language Among US Legislators During the COVID-19 Pandemic: Cross-sectional Study Guntuku, Sharath Chandra Purtle, Jonathan Meisel, Zachary F Merchant, Raina M Agarwal, Anish J Med Internet Res Original Paper BACKGROUND: As policy makers continue to shape the national and local responses to the COVID-19 pandemic, the information they choose to share and how they frame their content provide key insights into the public and health care systems. OBJECTIVE: We examined the language used by the members of the US House and Senate during the first 10 months of the COVID-19 pandemic and measured content and sentiment based on the tweets that they shared. METHODS: We used Quorum (Quorum Analytics Inc) to access more than 300,000 tweets posted by US legislators from January 1 to October 10, 2020. We used differential language analyses to compare the content and sentiment of tweets posted by legislators based on their party affiliation. RESULTS: We found that health care–related themes in Democratic legislators’ tweets focused on racial disparities in care (odds ratio [OR] 2.24, 95% CI 2.22-2.27; P<.001), health care and insurance (OR 1.74, 95% CI 1.7-1.77; P<.001), COVID-19 testing (OR 1.15, 95% CI 1.12-1.19; P<.001), and public health guidelines (OR 1.25, 95% CI 1.22-1.29; P<.001). The dominant themes in the Republican legislators’ discourse included vaccine development (OR 1.51, 95% CI 1.47-1.55; P<.001) and hospital resources and equipment (OR 1.22, 95% CI 1.18-1.25). Nonhealth care–related topics associated with a Democratic affiliation included protections for essential workers (OR 1.55, 95% CI 1.52-1.59), the 2020 election and voting (OR 1.31, 95% CI 1.27-1.35), unemployment and housing (OR 1.27, 95% CI 1.24-1.31), crime and racism (OR 1.22, 95% CI 1.18-1.26), public town halls (OR 1.2, 95% CI 1.16-1.23), the Trump Administration (OR 1.22, 95% CI 1.19-1.26), immigration (OR 1.16, 95% CI 1.12-1.19), and the loss of life (OR 1.38, 95% CI 1.35-1.42). The themes associated with the Republican affiliation included China (OR 1.89, 95% CI 1.85-1.92), small business assistance (OR 1.27, 95% CI 1.23-1.3), congressional relief bills (OR 1.23, 95% CI 1.2-1.27), press briefings (OR 1.22, 95% CI 1.19-1.26), and economic recovery (OR 1.2, 95% CI 1.16-1.23). CONCLUSIONS: Divergent language use on social media corresponds to the partisan divide in the first several months of the course of the COVID-19 public health crisis. JMIR Publications 2021-06-03 /pmc/articles/PMC8176946/ /pubmed/33939620 http://dx.doi.org/10.2196/27300 Text en ©Sharath Chandra Guntuku, Jonathan Purtle, Zachary F Meisel, Raina M Merchant, Anish Agarwal. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.06.2021. https://creativecommons.org/licenses/by/4.0/This 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 the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Guntuku, Sharath Chandra Purtle, Jonathan Meisel, Zachary F Merchant, Raina M Agarwal, Anish Partisan Differences in Twitter Language Among US Legislators During the COVID-19 Pandemic: Cross-sectional Study |
title | Partisan Differences in Twitter Language Among US Legislators During the COVID-19 Pandemic: Cross-sectional Study |
title_full | Partisan Differences in Twitter Language Among US Legislators During the COVID-19 Pandemic: Cross-sectional Study |
title_fullStr | Partisan Differences in Twitter Language Among US Legislators During the COVID-19 Pandemic: Cross-sectional Study |
title_full_unstemmed | Partisan Differences in Twitter Language Among US Legislators During the COVID-19 Pandemic: Cross-sectional Study |
title_short | Partisan Differences in Twitter Language Among US Legislators During the COVID-19 Pandemic: Cross-sectional Study |
title_sort | partisan differences in twitter language among us legislators during the covid-19 pandemic: cross-sectional study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8176946/ https://www.ncbi.nlm.nih.gov/pubmed/33939620 http://dx.doi.org/10.2196/27300 |
work_keys_str_mv | AT guntukusharathchandra partisandifferencesintwitterlanguageamonguslegislatorsduringthecovid19pandemiccrosssectionalstudy AT purtlejonathan partisandifferencesintwitterlanguageamonguslegislatorsduringthecovid19pandemiccrosssectionalstudy AT meiselzacharyf partisandifferencesintwitterlanguageamonguslegislatorsduringthecovid19pandemiccrosssectionalstudy AT merchantrainam partisandifferencesintwitterlanguageamonguslegislatorsduringthecovid19pandemiccrosssectionalstudy AT agarwalanish partisandifferencesintwitterlanguageamonguslegislatorsduringthecovid19pandemiccrosssectionalstudy |