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Predicting zip code-level vaccine hesitancy in US Metropolitan Areas using machine learning models on public tweets

Although the recent rise and uptake of COVID-19 vaccines in the United States has been encouraging, there continues to be significant vaccine hesitancy in various geographic and demographic clusters of the adult population. Surveys, such as the one conducted by Gallup over the past year, can be usef...

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Autores principales: Melotte, Sara, Kejriwal, Mayank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931269/
https://www.ncbi.nlm.nih.gov/pubmed/36812517
http://dx.doi.org/10.1371/journal.pdig.0000021
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author Melotte, Sara
Kejriwal, Mayank
author_facet Melotte, Sara
Kejriwal, Mayank
author_sort Melotte, Sara
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description Although the recent rise and uptake of COVID-19 vaccines in the United States has been encouraging, there continues to be significant vaccine hesitancy in various geographic and demographic clusters of the adult population. Surveys, such as the one conducted by Gallup over the past year, can be useful in determining vaccine hesitancy, but can be expensive to conduct and do not provide real-time data. At the same time, the advent of social media suggests that it may be possible to get vaccine hesitancy signals at an aggregate level, such as at the level of zip codes. Theoretically, machine learning models can be learned using socioeconomic (and other) features from publicly available sources. Experimentally, it remains an open question whether such an endeavor is feasible, and how it would compare to non-adaptive baselines. In this article, we present a proper methodology and experimental study for addressing this question. We use publicly available Twitter data collected over the previous year. Our goal is not to devise novel machine learning algorithms, but to rigorously evaluate and compare established models. Here we show that the best models significantly outperform non-learning baselines. They can also be set up using open-source tools and software.
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spelling pubmed-99312692023-02-16 Predicting zip code-level vaccine hesitancy in US Metropolitan Areas using machine learning models on public tweets Melotte, Sara Kejriwal, Mayank PLOS Digit Health Research Article Although the recent rise and uptake of COVID-19 vaccines in the United States has been encouraging, there continues to be significant vaccine hesitancy in various geographic and demographic clusters of the adult population. Surveys, such as the one conducted by Gallup over the past year, can be useful in determining vaccine hesitancy, but can be expensive to conduct and do not provide real-time data. At the same time, the advent of social media suggests that it may be possible to get vaccine hesitancy signals at an aggregate level, such as at the level of zip codes. Theoretically, machine learning models can be learned using socioeconomic (and other) features from publicly available sources. Experimentally, it remains an open question whether such an endeavor is feasible, and how it would compare to non-adaptive baselines. In this article, we present a proper methodology and experimental study for addressing this question. We use publicly available Twitter data collected over the previous year. Our goal is not to devise novel machine learning algorithms, but to rigorously evaluate and compare established models. Here we show that the best models significantly outperform non-learning baselines. They can also be set up using open-source tools and software. Public Library of Science 2022-04-07 /pmc/articles/PMC9931269/ /pubmed/36812517 http://dx.doi.org/10.1371/journal.pdig.0000021 Text en © 2022 Melotte, Kejriwal 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 author and source are credited.
spellingShingle Research Article
Melotte, Sara
Kejriwal, Mayank
Predicting zip code-level vaccine hesitancy in US Metropolitan Areas using machine learning models on public tweets
title Predicting zip code-level vaccine hesitancy in US Metropolitan Areas using machine learning models on public tweets
title_full Predicting zip code-level vaccine hesitancy in US Metropolitan Areas using machine learning models on public tweets
title_fullStr Predicting zip code-level vaccine hesitancy in US Metropolitan Areas using machine learning models on public tweets
title_full_unstemmed Predicting zip code-level vaccine hesitancy in US Metropolitan Areas using machine learning models on public tweets
title_short Predicting zip code-level vaccine hesitancy in US Metropolitan Areas using machine learning models on public tweets
title_sort predicting zip code-level vaccine hesitancy in us metropolitan areas using machine learning models on public tweets
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931269/
https://www.ncbi.nlm.nih.gov/pubmed/36812517
http://dx.doi.org/10.1371/journal.pdig.0000021
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