Geosocial Media's Early Warning Capabilities Across US County-Level Political Clusters: Observational Study.

IF 3.5 Q1 HEALTH CARE SCIENCES & SERVICES
JMIR infodemiology Pub Date : 2025-01-30 DOI:10.2196/58539
Dorian Arifi, Bernd Resch, Mauricio Santillana, Weihe Wendy Guan, Steffen Knoblauch, Sven Lautenbach, Thomas Jaenisch, Ivonne Morales, Clemens Havas
{"title":"Geosocial Media's Early Warning Capabilities Across US County-Level Political Clusters: Observational Study.","authors":"Dorian Arifi, Bernd Resch, Mauricio Santillana, Weihe Wendy Guan, Steffen Knoblauch, Sven Lautenbach, Thomas Jaenisch, Ivonne Morales, Clemens Havas","doi":"10.2196/58539","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The novel coronavirus disease (COVID-19) sparked significant health concerns worldwide, prompting policy makers and health care experts to implement nonpharmaceutical public health interventions, such as stay-at-home orders and mask mandates, to slow the spread of the virus. While these interventions proved essential in controlling transmission, they also caused substantial economic and societal costs and should therefore be used strategically, particularly when disease activity is on the rise. In this context, geosocial media posts (posts with an explicit georeference) have been shown to provide a promising tool for anticipating moments of potential health care crises. However, previous studies on the early warning capabilities of geosocial media data have largely been constrained by coarse spatial resolutions or short temporal scopes, with limited understanding of how local political beliefs may influence these capabilities.</p><p><strong>Objective: </strong>This study aimed to assess how the epidemiological early warning capabilities of geosocial media posts for COVID-19 vary over time and across US counties with differing political beliefs.</p><p><strong>Methods: </strong>We classified US counties into 3 political clusters, democrat, republican, and swing counties, based on voting data from the last 6 federal election cycles. In these clusters, we analyzed the early warning capabilities of geosocial media posts across 6 consecutive COVID-19 waves (February 2020-April 2022). We specifically examined the temporal lag between geosocial media signals and surges in COVID-19 cases, measuring both the number of days by which the geosocial media signals preceded the surges in COVID-19 cases (temporal lag) and the correlation between their respective time series.</p><p><strong>Results: </strong>The early warning capabilities of geosocial media data differed across political clusters and COVID-19 waves. On average, geosocial media posts preceded COVID-19 cases by 21 days in republican counties compared with 14.6 days in democrat counties and 24.2 days in swing counties. In general, geosocial media posts were preceding COVID-19 cases in 5 out of 6 waves across all political clusters. However, we observed a decrease over time in the number of days that posts preceded COVID-19 cases, particularly in democrat and republican counties. Furthermore, a decline in signal strength and the impact of trending topics presented challenges for the reliability of the early warning signals.</p><p><strong>Conclusions: </strong>This study provides valuable insights into the strengths and limitations of geosocial media data as an epidemiological early warning tool, particularly highlighting how they can change across county-level political clusters. Thus, these findings indicate that future geosocial media based epidemiological early warning systems might benefit from accounting for political beliefs. In addition, the impact of declining geosocial media signal strength over time and the role of trending topics for signal reliability in early warning systems need to be assessed in future research.</p>","PeriodicalId":73554,"journal":{"name":"JMIR infodemiology","volume":"5 ","pages":"e58539"},"PeriodicalIF":3.5000,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11826950/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR infodemiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2196/58539","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0

Abstract

Background: The novel coronavirus disease (COVID-19) sparked significant health concerns worldwide, prompting policy makers and health care experts to implement nonpharmaceutical public health interventions, such as stay-at-home orders and mask mandates, to slow the spread of the virus. While these interventions proved essential in controlling transmission, they also caused substantial economic and societal costs and should therefore be used strategically, particularly when disease activity is on the rise. In this context, geosocial media posts (posts with an explicit georeference) have been shown to provide a promising tool for anticipating moments of potential health care crises. However, previous studies on the early warning capabilities of geosocial media data have largely been constrained by coarse spatial resolutions or short temporal scopes, with limited understanding of how local political beliefs may influence these capabilities.

Objective: This study aimed to assess how the epidemiological early warning capabilities of geosocial media posts for COVID-19 vary over time and across US counties with differing political beliefs.

Methods: We classified US counties into 3 political clusters, democrat, republican, and swing counties, based on voting data from the last 6 federal election cycles. In these clusters, we analyzed the early warning capabilities of geosocial media posts across 6 consecutive COVID-19 waves (February 2020-April 2022). We specifically examined the temporal lag between geosocial media signals and surges in COVID-19 cases, measuring both the number of days by which the geosocial media signals preceded the surges in COVID-19 cases (temporal lag) and the correlation between their respective time series.

Results: The early warning capabilities of geosocial media data differed across political clusters and COVID-19 waves. On average, geosocial media posts preceded COVID-19 cases by 21 days in republican counties compared with 14.6 days in democrat counties and 24.2 days in swing counties. In general, geosocial media posts were preceding COVID-19 cases in 5 out of 6 waves across all political clusters. However, we observed a decrease over time in the number of days that posts preceded COVID-19 cases, particularly in democrat and republican counties. Furthermore, a decline in signal strength and the impact of trending topics presented challenges for the reliability of the early warning signals.

Conclusions: This study provides valuable insights into the strengths and limitations of geosocial media data as an epidemiological early warning tool, particularly highlighting how they can change across county-level political clusters. Thus, these findings indicate that future geosocial media based epidemiological early warning systems might benefit from accounting for political beliefs. In addition, the impact of declining geosocial media signal strength over time and the role of trending topics for signal reliability in early warning systems need to be assessed in future research.

地理社交媒体在美国县级政治集群中的预警能力:观察研究。
背景:新型冠状病毒病(COVID-19)在全球引发了重大的健康担忧,促使政策制定者和卫生保健专家实施非药物公共卫生干预措施,如居家令和戴口罩的规定,以减缓病毒的传播。虽然这些干预措施证明对控制传播至关重要,但它们也造成了巨大的经济和社会成本,因此应战略性地加以利用,特别是在疾病活动增加的情况下。在这方面,地理社交媒体帖子(带有明确地理参考的帖子)已被证明是预测潜在卫生保健危机时刻的一种很有前途的工具。然而,以往关于地理社交媒体数据预警能力的研究在很大程度上受到粗糙空间分辨率或短时间范围的限制,对当地政治信仰如何影响这些能力的理解有限。目的:本研究旨在评估地理社交媒体帖子对COVID-19的流行病学预警能力如何随时间和不同政治信仰的美国县而变化。方法:根据过去6个联邦选举周期的投票数据,我们将美国县分为3个政治集群,民主党、共和党和摇摆县。在这些聚类中,我们分析了连续6波COVID-19(2020年2月至2022年4月)期间地理社交媒体帖子的预警能力。我们专门研究了地理社交媒体信号与COVID-19病例激增之间的时间滞后,测量了地理社交媒体信号在COVID-19病例激增之前的天数(时间滞后)以及它们各自时间序列之间的相关性。结果:地理社交媒体数据的预警能力在政治集群和COVID-19浪潮之间存在差异。共和党县的地理社交媒体帖子平均比新冠肺炎病例早21天,民主党县为14.6天,摇摆县为24.2天。总体而言,在所有政治集群的6波浪潮中,有5波地缘社交媒体帖子出现在COVID-19病例之前。然而,我们观察到,随着时间的推移,在COVID-19病例出现之前的帖子天数有所减少,特别是在民主党和共和党县。此外,信号强度的下降和趋势话题的影响对预警信号的可靠性提出了挑战。结论:本研究对地理社交媒体数据作为流行病学早期预警工具的优势和局限性提供了有价值的见解,特别是强调了它们如何在县级政治集群中发生变化。因此,这些发现表明,未来基于地理社交媒体的流行病学预警系统可能会受益于考虑政治信仰。此外,未来的研究还需要评估地理社交媒体信号强度随时间下降的影响,以及趋势话题对预警系统信号可靠性的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
4.80
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信