{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:44:51Z","timestamp":1785545091747,"version":"3.56.0"},"reference-count":36,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n The global outbreak of COVID-19 has significantly impacted health care systems and has necessitated timely access to information for effective decision-making by health care authorities. Conventional methods for collecting patient data and analyzing virus mutations are resource-intensive. In the current era of rapid internet development, information on COVID-19 infections could be collected by a novel approach that leverages social media, particularly Twitter (subsequently rebranded X).<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n The aim of this study was to analyze the trending patterns of tweets containing information about various COVID-19 symptoms, explore their synchronization and correlation with conventional monitoring data, and provide insights into the evolution of the virus. We categorized tweet sentiments to understand the predictive power of negative emotions of different symptoms in anticipating the emergence of new Omicron subvariants and offering real-time assistance to affected individuals.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Relevant user tweets from 2022 containing information about COVID-19 symptoms were extracted from Twitter. Our fine-tuned RoBERTa model for sentiment analysis, achieving 99.7% accuracy for sentiment analysis, was used to categorize tweets as negative, positive, or neutral. Joinpoint regression analysis was used to examine the trends in weekly negative tweets related to COVID-19 symptoms, aligning these trends with the transition periods of SARS-CoV-2 Omicron subvariants from 2022. Real-time Twitter users with negative sentiments were geographically plotted. A total of 105,934 tweets related to fever, 120,257 to cough, 55,790 to headache, 101,220 to sore throat, 3410 to vomiting, and 5913 to diarrhea were collected.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The most prominent topics of discussion were fever, sore throat, and headache. The weekly average daily tweets exhibited different fluctuation patterns in different stages of subvariants. Specifically, fever-related negative tweets were more sensitive to Omicron subvariant evolution, while discussions of other symptoms declined and stabilized following the emergence of the BA.2 variant. Negative discussions about fever rose to nearly 40% at the beginning of 2022 and showed 2 distinct peaks during the absolute dominance of BA.2 and BA.5, respectively. Headache and throat-related negative sentiment exhibited the highest levels among the analyzed symptoms. Tweets containing geographic information accounted for 1.5% (1351\/391,508) of all collected data, with negative sentiment users making up 0.35% (5873\/391,508) of all related tweets.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n This study underscores the potential of using social media, particularly tweet trends, for real-time analysis of COVID-19 infections and has demonstrated correlations with major symptoms. The degree of negative emotions expressed in tweets is valuable in predicting the emergence of new Omicron subvariants of COVID-19 and facilitating the provision of timely assistance to affected individuals.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/66237","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T20:44:03Z","timestamp":1758660243000},"page":"e66237-e66237","source":"Crossref","is-referenced-by-count":2,"title":["Surveillance of Twitter Data on COVID-19 Symptoms During the Omicron Variant Period: A Sentiment Analysis"],"prefix":"10.2196","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1541-1307","authenticated-orcid":false,"given":"Kaiyue","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6254-0041","authenticated-orcid":false,"given":"Zhaojin","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8126-2852","authenticated-orcid":false,"given":"Yujie","family":"Ai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3918-9784","authenticated-orcid":false,"given":"An-Ran","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5082-8432","authenticated-orcid":false,"given":"Anlin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3081-9492","authenticated-orcid":false,"given":"Ziyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5798-2769","authenticated-orcid":false,"given":"Yittie Yi Ting","family":"Tse","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0497-1279","authenticated-orcid":false,"given":"Xinyu","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7806-1263","authenticated-orcid":false,"given":"Taoran","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9473-6604","authenticated-orcid":false,"given":"Chuxi","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3931-5013","authenticated-orcid":false,"given":"Jian","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8846-7515","authenticated-orcid":false,"given":"Wai-kit","family":"Ming","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"issue":"50","key":"R1","doi-asserted-by":"publisher","first-page":"21701","DOI":"10.1073\/pnas.1006219107","article-title":"Global capacity for emerging infectious disease detection","volume":"107","author":"Chan","journal-title":"Proc Natl Acad Sci USA"},{"issue":"1","key":"R2","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1111\/1468-0009.12038","article-title":"Social media and internet-based data in global systems for public health surveillance: a systematic review","volume":"92","author":"Velasco","journal-title":"Milbank Q"},{"key":"R3","unstructured":"Strengthening surveillance of and response to foodborne diseases: a practical manual. 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