{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T06:52:23Z","timestamp":1783234343499,"version":"3.54.6"},"reference-count":22,"publisher":"JMIR Publications Inc.","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"\n Background<\/jats:title>\n Health-related social media data are increasingly being used in disease surveillance studies. In particular, surveillance of infectious diseases such as influenza has demonstrated high correlations between the number of social media posts mentioning the disease and the number of patients who went to the hospital and were diagnosed with the disease. However, the prevalence of some diseases, such as allergic rhinitis, cannot be estimated based on the number of patients alone. Specifically, individuals with allergic rhinitis typically self-medicate by taking over-the-counter (OTC) medications without going to the hospital. Although allergic rhinitis is not a life-threatening disease, it represents a major social problem because it reduces people\u2019s quality of life, making it essential to understand its prevalence and people\u2019s motives for self-medication behavior.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aims to explore the relationship between the number of social media posts mentioning the main symptoms of allergic rhinitis and the sales volume of OTC rhinitis medications in Japan.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We collected tweets over 4 years (from 2017 to 2020) that included keywords corresponding to the main nasal symptoms of allergic rhinitis: \u201csneezing,\u201d \u201crunny nose,\u201d and \u201cstuffy nose.\u201d We also obtained the sales volume of OTC drugs, including oral medications and nasal sprays, for the same period. We then calculated the Pearson correlation coefficient between time series data on the number of tweets per week and time series data on the sales volume of OTC drugs per week.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The results showed a much higher correlation (r=0.8432) between the time series data on the number of tweets mentioning \u201cstuffy nose\u201d and the time series data on the sales volume of nasal sprays than for the other two symptoms. There was also a high correlation (r=0.9317) between the seasonal components of these time series data.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n We investigated the relationships between social media data and behavioral patterns, such as OTC drug sales volume. Exploring these relationships can help us understand the prevalence of allergic rhinitis and the motives for self-care treatment using social media data, which would be useful as a marketing indicator to reduce the number of out-of-stocks in stores, provide (sell) rhinitis medicines to consumers in a stable manner, and reduce the loss of sales opportunities. In the future, in-depth investigations are required to estimate sales volume using social media data, and future research could investigate other diseases and countries.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/33941","type":"journal-article","created":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T02:22:09Z","timestamp":1641262929000},"page":"e33941","source":"Crossref","is-referenced-by-count":11,"title":["Exploring Relationships Between Tweet Numbers and Over-the-counter Drug Sales for Allergic Rhinitis: Retrospective Analysis"],"prefix":"10.2196","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9371-1340","authenticated-orcid":false,"given":"Shoko","family":"Wakamiya","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2505-354X","authenticated-orcid":false,"given":"Osamu","family":"Morimoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8692-7904","authenticated-orcid":false,"given":"Katsuhiro","family":"Omichi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9715-7490","authenticated-orcid":false,"given":"Hideyuki","family":"Hara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8166-9360","authenticated-orcid":false,"given":"Ichiro","family":"Kawase","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8251-3691","authenticated-orcid":false,"given":"Ryuji","family":"Koshiba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0201-3609","authenticated-orcid":false,"given":"Eiji","family":"Aramaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2022,2,2]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.3765"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.4898"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.1157"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0139701"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1142\/9789814749411_0043"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2016.05.005"},{"key":"ref7","unstructured":"AramakiEMaskawaSMoritaMTwitter catches the flu: Detecting influenza epidemics using TwitterProceedings of the 2011 Conference on Empirical Methods in Natural Language Processing2011072011 Conference on Empirical Methods in Natural Language ProcessingJuly 27-31, 2011Edinburgh, Scotland, UK15681576"},{"key":"ref8","unstructured":"PaulMDredzeMYou are what you tweet: Analyzing Twitter for public healthProceedings of the International AAAI Conference on Web and Social Media20110803Fifth International AAAI Conference on Weblogs and Social Media17-21 July 2011Barcelona, 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Inc2021-08-25https:\/\/www.intage.co.jp\/english\/service\/platform\/sdi\/"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.2196\/10450"},{"key":"ref21","unstructured":"Press release [in Japanese]NOVARTIS2021-08-25https:\/\/www.novartis.co.jp\/news\/media-releases\/prkk20210122"},{"key":"ref22","unstructured":"statsmodels v0.13.12021-08-25https:\/\/www.statsmodels.org\/stable\/generated\/statsmodels.tsa.seasonal.seasonal_decompose.html"}],"container-title":["JMIR Formative 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