{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T20:56:15Z","timestamp":1786136175145,"version":"build-2736575974"},"reference-count":45,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Infodemiology"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Influenza seasons may be associated with increased anxiety-related expressions on social media. Social media can reflect population-level emotional expression patterns in real time.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n The aim of the study is to characterize diurnal and full-season dynamics of anxiety-related language during the 2023\u20102024 influenza season in China and its association with influenza activity.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n We retrieved Sina Weibo posts in February 2025 covering September 4, 2023, to April 28, 2024. Posts containing\n Diagnostic and Statistical Manual of Mental Disorders<\/jats:italic>\n (\n DSM<\/jats:italic>\n )\u2013based anxiety terms were cleaned and deduplicated (N=169,728 \u2192 106,440). We first linked weekly influenza incidence with anxiety-related postings. Then, we plotted diurnal patterns by epidemiologic phase, conducted supplementary within-sample hourly normalization analyses, and modeled longitudinal symptom trajectories using ARIMA (autoregressive integrated moving average) and supplementary ARIMAX (autoregressive integrated moving average with exogenous regressors) time-series models.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Anxiety-related posts closely followed influenza activity, surging during the outbreak and peak phases and remaining elevated even after influenza declined. Initial Spearman correlation analyses showed significant negative associations for irritability (\n r<\/jats:italic>\n =\u22120.413;\n P<\/jats:italic>\n =.02) and restlessness or feeling keyed up or on edge (\n r<\/jats:italic>\n =\u20130.396;\n P<\/jats:italic>\n =.02). However, supplementary ARIMAX analyses further revealed that being easily fatigued and difficulty concentrating or mind going blank exhibited more stable positive temporal associations with influenza activity after controlling for autocorrelation and lagged effects. Diurnal patterns shifted across stages, showing mild early-evening variation during the outbreak, clear morning peaks with secondary afternoon and evening rises during prevalence and decline, and morning-afternoon concentration in the end stage. Supplementary within-sample hourly normalization analyses showed that the major diurnal structures remained generally stable after normalization. ARIMA time-series analysis revealed that irritability and being easily fatigued consistently dominated the discussions, whereas others remained at relatively low levels. Out-of-sample forecasting based on a chronological 80% training and 20% testing split suggested generally stable short-term trajectories, with being easily fatigued showing a slight increase.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n This study demonstrates how social media can capture diurnal and seasonal fluctuations of anxiety symptoms associated with influenza activity, advancing understanding of affective dynamics in population health.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/88849","type":"journal-article","created":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T23:40:07Z","timestamp":1782258007000},"page":"e88849-e88849","source":"Crossref","is-referenced-by-count":0,"title":["Temporal Dynamics of Influenza-Associated Anxiety Symptom Linguistic Markers on Weibo (2023-2024): Observational Study"],"prefix":"10.2196","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4654-6936","authenticated-orcid":false,"given":"Yifan","family":"Ou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9759-3938","authenticated-orcid":false,"given":"Gert-Jan de","family":"Bruijn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4281-489X","authenticated-orcid":false,"given":"Peter Johannes","family":"Schulz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,8,7]]},"reference":[{"key":"R1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cobeha.2017.07.005","article-title":"Detecting depression and mental illness on social media: an integrative review","volume":"18","author":"Guntuku","journal-title":"Curr Opin Behav Sci"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2025.119752","article-title":"Comorbidities and emotions\u2014unpacking the sentiments of pediatric patients with multiple long-term conditions through social media feedback: a large language model-driven study","volume":"388","author":"Oluwalade","journal-title":"J Affect Disord"},{"issue":"1","key":"R3","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1177\/14614448211058850","article-title":"Shifting moods on Sina Weibo: the first 12\u2009weeks of COVID-19 in Wuhan","volume":"26","author":"Zheng","journal-title":"New Media Soc"},{"key":"R4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2025.119385","article-title":"Identifying possible influence factors for depression through social media","volume":"385","author":"Wang","journal-title":"J Affect Disord"},{"issue":"1","key":"R5","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1609\/icwsm.v7i1.14432","article-title":"Predicting depression via social media","volume":"7","author":"De Choudhury","journal-title":"ICWSM"},{"issue":"7","key":"R6","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.7452","article-title":"Ontology-based approach to social data sentiment analysis: detection of adolescent depression signals","volume":"19","author":"Jung","journal-title":"J Med Internet Res"},{"issue":"Pt A","key":"R7","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.jad.2020.11.040","article-title":"Detecting changes in attitudes toward depression on Chinese social media: a text analysis","volume":"280","author":"Yu","journal-title":"J Affect Disord"},{"key":"R8","doi-asserted-by":"publisher","DOI":"10.2196\/45777","article-title":"Public attitudes toward anxiety disorder on Sina Weibo: content analysis","volume":"25","author":"Zhu","journal-title":"J Med Internet Res"},{"issue":"1","key":"R9","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1177\/14614448211054377","article-title":"Online social engagement, depression, and anxiety among older adults","volume":"26","author":"Hofer","journal-title":"New Media Soc"},{"issue":"6","key":"R10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3422824","article-title":"Using social media for mental health surveillance","volume":"53","author":"Skaik","journal-title":"ACM Comput Surv"},{"key":"R11","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2020.586222","article-title":"Facing loneliness and anxiety during the COVID-19 isolation: the role of excessive social media use in a sample of Italian adults","volume":"11","author":"Boursier","journal-title":"Front Psychiatry"},{"key":"R12","doi-asserted-by":"crossref","unstructured":"Hung LP Tadius EM . 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