{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T06:18:09Z","timestamp":1781763489798,"version":"3.54.5"},"reference-count":43,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:00:00Z","timestamp":1748822400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000076","name":"Directorate for Biological Sciences","doi-asserted-by":"publisher","award":["2109647"],"award-info":[{"award-number":["2109647"]}],"id":[{"id":"10.13039\/100000076","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000083","name":"Directorate for Computer and Information Science and Engineering","doi-asserted-by":"publisher","award":["2302970"],"award-info":[{"award-number":["2302970"]}],"id":[{"id":"10.13039\/100000083","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000083","name":"Directorate for Computer and Information Science and Engineering","doi-asserted-by":"publisher","award":["2302968"],"award-info":[{"award-number":["2302968"]}],"id":[{"id":"10.13039\/100000083","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Social media data has shown potential for identifying infectious disease outbreaks faster than official records of disease incidence. We examine spatial, temporal, and spatiotemporal relationships between COVID-19-related microblog sentiment and COVID-19 cases over space and time to investigate whether microblog-derived sentiment can be used for local infectious disease outbreak early warning. Therefore, we measure the sentiment of 56,755,894 COVID-19 related microblogs (tweets) from the microblogging platform X. We group these tweets by county and by calendar week to investigate spatial and temporal correlation between sentiment and observed cases (in the corresponding county and week). Our temporal analysis shows a significant negative correlation between sentiment and cases between June and September 2020. During this time, tweet sentiment could have served as an early warning for new COVID-19 outbreaks. Our spatial analysis shows that the East of the United States exhibits a significant negative correlation between Sentiment and Cases while the West exhibits a significant positive correlation. In these regions, Tweet Sentiment could have been used as an early warning signal for new outbreaks. Our spatiotemporal analysis discovers even stronger correlations in certain regions during certain time periods. If we could understand when, where, and why this correlation is strong, then we may be able to leverage social media as a successful early warning system.<\/jats:p>","DOI":"10.1371\/journal.pone.0325166","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T17:38:15Z","timestamp":1748885895000},"page":"e0325166","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Towards using Tweet sentiment for infectious disease detection"],"prefix":"10.1371","volume":"20","author":[{"given":"James","family":"Stassinos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1145-0608","authenticated-orcid":true,"given":"Taylor","family":"Anderson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Z\u00fcfle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,6,2]]},"reference":[{"issue":"2","key":"pone.0325166.ref001","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/s10729-020-09542-0","article-title":"From predictions to prescriptions: a data-driven response to COVID-19","volume":"24","author":"D Bertsimas","year":"2021","journal-title":"Health Care Manag Sci"},{"issue":"11","key":"pone.0325166.ref002","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0259031","article-title":"Change of human mobility during COVID-19: a United States case study","volume":"16","author":"J Elarde","year":"2021","journal-title":"PLoS One"},{"issue":"23","key":"pone.0325166.ref003","doi-asserted-by":"crossref","first-page":"15716","DOI":"10.3390\/ijerph192315716","article-title":"Social behavior and COVID-19: analysis of the social factors behind compliance with interventions across the United States","volume":"19","author":"M Maleki","year":"2022","journal-title":"Int J Environ Res Public Health"},{"key":"pone.0325166.ref004","unstructured":"CDC. 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analysis to study diseases dynamics.","volume-title":"International Conference on Information Technology in Bio- and Medical Informatics","author":"V Carchiolo","year":"2015"},{"key":"pone.0325166.ref025","doi-asserted-by":"crossref","unstructured":"Byrd K, Mansurov A, Baysal O. Mining Twitter data for influenza detection and surveillance. In: Proceedings of the International Workshop on Software Engineering in Healthcare Systems. 2016, pp. 43\u20139.","DOI":"10.1145\/2897683.2897693"},{"key":"pone.0325166.ref026","unstructured":"Aramaki E, Maskawa S, Morita M. Twitter catches the flu: detecting influenza epidemics using Twitter. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics; 2011, pp. 1568\u201376."},{"key":"pone.0325166.ref027","doi-asserted-by":"crossref","unstructured":"Doan S, OhnoMachado L, Collier N. Enhancing Twitter data analysis with simple semantic filtering: example in tracking influenza-like illnesses. In: 2012 IEEE 2nd International Conference on Healthcare Informatics, Imaging and Systems Biology. IEEE; 2012, pp. 62\u201371.","DOI":"10.1109\/HISB.2012.21"},{"issue":"12","key":"pone.0325166.ref028","doi-asserted-by":"crossref","DOI":"10.2196\/21418","article-title":"Social media insights into US mental health during the COVID-19 pandemic: longitudinal analysis of Twitter data","volume":"22","author":"D Valdez","year":"2020","journal-title":"J Med Internet Res"},{"issue":"1","key":"pone.0325166.ref029","doi-asserted-by":"crossref","first-page":"8514","DOI":"10.1038\/s41598-021-88010-3","article-title":"Integrating online and offline data for crisis management: online geolocalized emotion, policy response, and local mobility during the COVID crisis","volume":"11","author":"S Feng","year":"2021","journal-title":"Sci Rep"},{"issue":"1","key":"pone.0325166.ref030","doi-asserted-by":"crossref","first-page":"8","DOI":"10.3390\/data7010008","article-title":"TBCOV: two billion multilingual COVID-19 Tweets with sentiment, entity, geo, and gender labels","volume":"7","author":"M Imran","year":"2022","journal-title":"Data"},{"key":"pone.0325166.ref031","first-page":"627","article-title":"Sentiment analysis and subjectivity.","volume-title":"Handbook of natural language processing","author":"B. Liu","year":"2010"},{"key":"pone.0325166.ref032","unstructured":"Loria S. Textblob: simplified text processing \u2014 textblob 0.16.0 documentation. Available from: https:\/\/textblob.readthedocs.io\/en\/dev\/"},{"issue":"1","key":"pone.0325166.ref033","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1609\/icwsm.v8i1.14550","article-title":"VADER: a parsimonious rule-based model for sentiment analysis of social media text","volume":"8","author":"C Hutto","year":"2014","journal-title":"ICWSM"},{"key":"pone.0325166.ref034","unstructured":"Nielsen F\u00c5. AFINN sentiment analysis in Python. Available from: https:\/\/github.com\/fnielsen\/afinn. Accessed January 17, 2025."},{"key":"pone.0325166.ref035","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780195138276.001.0001","volume-title":"Principles and practice of public health surveillance","author":"SM Teutsch","year":"2000"},{"issue":"1","key":"pone.0325166.ref036","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1145\/3404820.3404823","article-title":"GeoCoV19: a dataset of hundreds of millions of multilingual COVID-19 tweets with location information.","volume":"12","author":"U Qazi","year":"2020","journal-title":"SIGSPATIAL Special"},{"key":"pone.0325166.ref037","unstructured":"USAFacts. US COVID-19 cases and deaths by state. Available from: https:\/\/usafacts.org\/visualizations\/coronavirus-covid-19-spread-map\/. Accessed January 17, 2025."},{"key":"pone.0325166.ref038","unstructured":"University of Wisconsin Population Health Institute. County health rankings and roadmap. Available from: https:\/\/www.countyhealthrankings.org\/."},{"issue":"1","key":"pone.0325166.ref039","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1609\/icwsm.v5i1.14168","article-title":"Understanding the demographics of Twitter users","volume":"5","author":"A Mislove","year":"2021","journal-title":"ICWSM"},{"issue":"9","key":"pone.0325166.ref040","doi-asserted-by":"crossref","DOI":"10.1136\/bmjgh-2021-006798","article-title":"Media bias exposure and the incidence of COVID-19 in the USA","volume":"6","author":"J Spiteri","year":"2021","journal-title":"BMJ Glob Health"},{"issue":"10","key":"pone.0325166.ref041","doi-asserted-by":"crossref","first-page":"220716","DOI":"10.1098\/rsos.220716","article-title":"The voice of few, the opinions of many: evidence of social biases in Twitter COVID-19 fake news sharing","volume":"9","author":"P Castioni","year":"2022","journal-title":"R Soc Open Sci"},{"issue":"4","key":"pone.0325166.ref042","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1109\/TCSS.2021.3051189","article-title":"COVIDSenti: a large-scale benchmark Twitter data set for COVID-19 sentiment analysis","volume":"8","author":"U Naseem","year":"2021","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"12","key":"pone.0325166.ref043","doi-asserted-by":"crossref","first-page":"6272","DOI":"10.3390\/ijerph18126272","article-title":"Analyzing Twitter data to evaluate people\u2019s attitudes towards public health policies and events in the era of COVID-19","volume":"18","author":"MH Tsai","year":"2021","journal-title":"Int J Environ Res Public Health"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0325166","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T17:38:26Z","timestamp":1748885906000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0325166"}},"subtitle":[],"editor":[{"given":"Li-Pang","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,6,2]]},"references-count":43,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6,2]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0325166","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,2]]}}}