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Characteristics of Twitter Use by State Medicaid Programs in the United States: Machine Learning Approach
BACKGROUND: Twitter is a potentially valuable tool for public health officials and state Medicaid programs in the United States, which provide public health insurance to 72 million Americans. OBJECTIVE: We aim to characterize how Medicaid agencies and managed care organization (MCO) health plans are...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7459428/ https://www.ncbi.nlm.nih.gov/pubmed/32804085 http://dx.doi.org/10.2196/18401 |
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author | Zhu, Jane M Sarker, Abeed Gollust, Sarah Merchant, Raina Grande, David |
author_facet | Zhu, Jane M Sarker, Abeed Gollust, Sarah Merchant, Raina Grande, David |
author_sort | Zhu, Jane M |
collection | PubMed |
description | BACKGROUND: Twitter is a potentially valuable tool for public health officials and state Medicaid programs in the United States, which provide public health insurance to 72 million Americans. OBJECTIVE: We aim to characterize how Medicaid agencies and managed care organization (MCO) health plans are using Twitter to communicate with the public. METHODS: Using Twitter’s public application programming interface, we collected 158,714 public posts (“tweets”) from active Twitter profiles of state Medicaid agencies and MCOs, spanning March 2014 through June 2019. Manual content analyses identified 5 broad categories of content, and these coded tweets were used to train supervised machine learning algorithms to classify all collected posts. RESULTS: We identified 15 state Medicaid agencies and 81 Medicaid MCOs on Twitter. The mean number of followers was 1784, the mean number of those followed was 542, and the mean number of posts was 2476. Approximately 39% of tweets came from just 10 accounts. Of all posts, 39.8% (63,168/158,714) were classified as general public health education and outreach; 23.5% (n=37,298) were about specific Medicaid policies, programs, services, or events; 18.4% (n=29,203) were organizational promotion of staff and activities; and 11.6% (n=18,411) contained general news and news links. Only 4.5% (n=7142) of posts were responses to specific questions, concerns, or complaints from the public. CONCLUSIONS: Twitter has the potential to enhance community building, beneficiary engagement, and public health outreach, but appears to be underutilized by the Medicaid program. |
format | Online Article Text |
id | pubmed-7459428 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-74594282020-09-03 Characteristics of Twitter Use by State Medicaid Programs in the United States: Machine Learning Approach Zhu, Jane M Sarker, Abeed Gollust, Sarah Merchant, Raina Grande, David J Med Internet Res Short Paper BACKGROUND: Twitter is a potentially valuable tool for public health officials and state Medicaid programs in the United States, which provide public health insurance to 72 million Americans. OBJECTIVE: We aim to characterize how Medicaid agencies and managed care organization (MCO) health plans are using Twitter to communicate with the public. METHODS: Using Twitter’s public application programming interface, we collected 158,714 public posts (“tweets”) from active Twitter profiles of state Medicaid agencies and MCOs, spanning March 2014 through June 2019. Manual content analyses identified 5 broad categories of content, and these coded tweets were used to train supervised machine learning algorithms to classify all collected posts. RESULTS: We identified 15 state Medicaid agencies and 81 Medicaid MCOs on Twitter. The mean number of followers was 1784, the mean number of those followed was 542, and the mean number of posts was 2476. Approximately 39% of tweets came from just 10 accounts. Of all posts, 39.8% (63,168/158,714) were classified as general public health education and outreach; 23.5% (n=37,298) were about specific Medicaid policies, programs, services, or events; 18.4% (n=29,203) were organizational promotion of staff and activities; and 11.6% (n=18,411) contained general news and news links. Only 4.5% (n=7142) of posts were responses to specific questions, concerns, or complaints from the public. CONCLUSIONS: Twitter has the potential to enhance community building, beneficiary engagement, and public health outreach, but appears to be underutilized by the Medicaid program. JMIR Publications 2020-08-17 /pmc/articles/PMC7459428/ /pubmed/32804085 http://dx.doi.org/10.2196/18401 Text en ©Jane M Zhu, Abeed Sarker, Sarah Gollust, Raina Merchant, David Grande. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 17.08.2020. 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 http://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Short Paper Zhu, Jane M Sarker, Abeed Gollust, Sarah Merchant, Raina Grande, David Characteristics of Twitter Use by State Medicaid Programs in the United States: Machine Learning Approach |
title | Characteristics of Twitter Use by State Medicaid Programs in the United States: Machine Learning Approach |
title_full | Characteristics of Twitter Use by State Medicaid Programs in the United States: Machine Learning Approach |
title_fullStr | Characteristics of Twitter Use by State Medicaid Programs in the United States: Machine Learning Approach |
title_full_unstemmed | Characteristics of Twitter Use by State Medicaid Programs in the United States: Machine Learning Approach |
title_short | Characteristics of Twitter Use by State Medicaid Programs in the United States: Machine Learning Approach |
title_sort | characteristics of twitter use by state medicaid programs in the united states: machine learning approach |
topic | Short Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7459428/ https://www.ncbi.nlm.nih.gov/pubmed/32804085 http://dx.doi.org/10.2196/18401 |
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