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Twitter Sentiment Predicts Affordable Care Act Marketplace Enrollment
BACKGROUND: Traditional metrics of the impact of the Affordable Care Act (ACA) and health insurance marketplaces in the United States include public opinion polls and marketplace enrollment, which are published with a lag of weeks to months. In this rapidly changing environment, a real-time baromete...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications Inc.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4376155/ https://www.ncbi.nlm.nih.gov/pubmed/25707038 http://dx.doi.org/10.2196/jmir.3812 |
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author | Wong, Charlene A Sap, Maarten Schwartz, Andrew Town, Robert Baker, Tom Ungar, Lyle Merchant, Raina M |
author_facet | Wong, Charlene A Sap, Maarten Schwartz, Andrew Town, Robert Baker, Tom Ungar, Lyle Merchant, Raina M |
author_sort | Wong, Charlene A |
collection | PubMed |
description | BACKGROUND: Traditional metrics of the impact of the Affordable Care Act (ACA) and health insurance marketplaces in the United States include public opinion polls and marketplace enrollment, which are published with a lag of weeks to months. In this rapidly changing environment, a real-time barometer of public opinion with a mechanism to identify emerging issues would be valuable. OBJECTIVE: We sought to evaluate Twitter’s role as a real-time barometer of public sentiment on the ACA and to determine if Twitter sentiment (the positivity or negativity of tweets) could be predictive of state-level marketplace enrollment. METHODS: We retrospectively collected 977,303 ACA-related tweets in March 2014 and then tested a correlation of Twitter sentiment with marketplace enrollment by state. RESULTS: A 0.10 increase in the sentiment score was associated with an 8.7% increase in enrollment at the state level (95% CI 1.32-16.13; P=.02), a correlation that remained significant when adjusting for state Medicaid expansion (P=.02) or use of a state-based marketplace (P=.03). CONCLUSIONS: This correlation indicates Twitter’s potential as a real-time monitoring strategy for future marketplace enrollment periods; marketplaces could systematically track Twitter sentiment to more rapidly identify enrollment changes and potentially emerging issues. As a repository of free and accessible consumer-generated opinions, this study reveals a novel role for Twitter in the health policy landscape. |
format | Online Article Text |
id | pubmed-4376155 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | JMIR Publications Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-43761552015-04-02 Twitter Sentiment Predicts Affordable Care Act Marketplace Enrollment Wong, Charlene A Sap, Maarten Schwartz, Andrew Town, Robert Baker, Tom Ungar, Lyle Merchant, Raina M J Med Internet Res Original Paper BACKGROUND: Traditional metrics of the impact of the Affordable Care Act (ACA) and health insurance marketplaces in the United States include public opinion polls and marketplace enrollment, which are published with a lag of weeks to months. In this rapidly changing environment, a real-time barometer of public opinion with a mechanism to identify emerging issues would be valuable. OBJECTIVE: We sought to evaluate Twitter’s role as a real-time barometer of public sentiment on the ACA and to determine if Twitter sentiment (the positivity or negativity of tweets) could be predictive of state-level marketplace enrollment. METHODS: We retrospectively collected 977,303 ACA-related tweets in March 2014 and then tested a correlation of Twitter sentiment with marketplace enrollment by state. RESULTS: A 0.10 increase in the sentiment score was associated with an 8.7% increase in enrollment at the state level (95% CI 1.32-16.13; P=.02), a correlation that remained significant when adjusting for state Medicaid expansion (P=.02) or use of a state-based marketplace (P=.03). CONCLUSIONS: This correlation indicates Twitter’s potential as a real-time monitoring strategy for future marketplace enrollment periods; marketplaces could systematically track Twitter sentiment to more rapidly identify enrollment changes and potentially emerging issues. As a repository of free and accessible consumer-generated opinions, this study reveals a novel role for Twitter in the health policy landscape. JMIR Publications Inc. 2015-02-23 /pmc/articles/PMC4376155/ /pubmed/25707038 http://dx.doi.org/10.2196/jmir.3812 Text en ©Charlene A Wong, Maarten Sap, Andrew Schwartz, Robert Town, Tom Baker, Lyle Ungar, Raina M Merchant. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 23.02.2015. http://creativecommons.org/licenses/by/2.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), 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 http://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Wong, Charlene A Sap, Maarten Schwartz, Andrew Town, Robert Baker, Tom Ungar, Lyle Merchant, Raina M Twitter Sentiment Predicts Affordable Care Act Marketplace Enrollment |
title | Twitter Sentiment Predicts Affordable Care Act Marketplace Enrollment |
title_full | Twitter Sentiment Predicts Affordable Care Act Marketplace Enrollment |
title_fullStr | Twitter Sentiment Predicts Affordable Care Act Marketplace Enrollment |
title_full_unstemmed | Twitter Sentiment Predicts Affordable Care Act Marketplace Enrollment |
title_short | Twitter Sentiment Predicts Affordable Care Act Marketplace Enrollment |
title_sort | twitter sentiment predicts affordable care act marketplace enrollment |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4376155/ https://www.ncbi.nlm.nih.gov/pubmed/25707038 http://dx.doi.org/10.2196/jmir.3812 |
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