{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T14:47:18Z","timestamp":1758811638832},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T00:00:00Z","timestamp":1622073600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,27]]},"abstract":"<jats:p>The exhaustive automatic detection of symptoms in social media posts is made difficult by the presence of colloquial expressions, misspellings and inflected forms of words. The detection of self-reported symptoms is of major importance for emergent diseases like the Covid-19. In this study, we aimed to (1) develop an algorithm based on fuzzy matching to detect symptoms in tweets, (2) establish a comprehensive list of Covid-19-related symptoms and (3) evaluate the fuzzy matching for Covid-19-related symptom detection in French tweets. The Covid-19-related symptom list was built based on the aggregation of different data sources. French Covid-19-related tweets were automatically extracted using a dedicated data broker during the first wave of the pandemic in France. The fuzzy matching parameters were finetuned using all symptoms from MedDRA and then evaluated on a subset of 5000 Covid-19-related tweets in French for the detection of symptoms from our Covid-19-related list. The fuzzy matching improved the detection by the addition of 42% more correct matches with an 81% precision.<\/jats:p>","DOI":"10.3233\/shti210308","type":"book-chapter","created":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T14:35:10Z","timestamp":1622126110000},"source":"Crossref","is-referenced-by-count":1,"title":["Fuzzy Matching for Symptom Detection in Tweets: Application to Covid-19 During the First Wave of the Pandemic in France"],"prefix":"10.3233","author":[{"given":"Carole","family":"Faviez","sequence":"first","affiliation":[{"name":"Centre de Recherche des Cordeliers, Sorbonne Universit\u00e9, INSERM, Universit\u00e9 de Paris, F-75006, Paris, France"}]},{"given":"Pierre","family":"Foulqui\u00e9","sequence":"additional","affiliation":[{"name":"Kap Code, Paris, France"}]},{"given":"Xiaoyi","family":"Chen","sequence":"additional","affiliation":[{"name":"Centre de Recherche des Cordeliers, Sorbonne Universit\u00e9, INSERM, Universit\u00e9 de Paris, F-75006, Paris, France"}]},{"given":"Adel","family":"Mebarki","sequence":"additional","affiliation":[{"name":"Kap Code, Paris, France"}]},{"given":"Sophie","family":"Quennelle","sequence":"additional","affiliation":[{"name":"Centre de Recherche des Cordeliers, Sorbonne Universit\u00e9, INSERM, Universit\u00e9 de Paris, F-75006, Paris, France"},{"name":"M3C-Necker,H\u00f4pital Necker-Enfants Malades, AP-HP, F-75015, Paris, France"}]},{"given":"Nathalie","family":"Texier","sequence":"additional","affiliation":[{"name":"Kap Code, Paris, France"}]},{"given":"Sandrine","family":"Katsahian","sequence":"additional","affiliation":[{"name":"Centre de Recherche des Cordeliers, Sorbonne Universit\u00e9, INSERM, Universit\u00e9 de Paris, F-75006, Paris, France"},{"name":"H\u00f4pital europ\u00e9en Georges Pompidou, Unit\u00e9 d\u2019\u00e9pid\u00e9miologie et de recherche clinique, AP-HP, F-75015, Paris, France"}]},{"given":"St\u00e9phane","family":"Schuck","sequence":"additional","affiliation":[{"name":"Kap Code, Paris, France"}]},{"given":"Anita","family":"Burgun","sequence":"additional","affiliation":[{"name":"Centre de Recherche des Cordeliers, Sorbonne Universit\u00e9, INSERM, Universit\u00e9 de Paris, F-75006, Paris, France"},{"name":"H\u00f4pital Necker-Enfants Malades, D\u00e9partement d\u2019informatique m\u00e9dicale, AP-HP, F-75015, Paris, France"},{"name":"PaRis Artificial Intelligence Research InstitutE (PRAIRIE), France"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Public Health and Informatics"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI210308","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T13:15:36Z","timestamp":1635167736000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI210308"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,27]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti210308","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,27]]}}}