{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:53:01Z","timestamp":1782841981189,"version":"3.54.5"},"reference-count":21,"publisher":"JMIR Publications Inc.","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>A substantial amount of COVID-19\u2013related data is generated by Twitter users every day. Self-reports of COVID-19 symptoms on Twitter can reveal a great deal about the disease and its prevalence in the community. In particular, self-reports can be used as a valuable resource to learn more about common symptoms and whether their order of appearance differs among different groups in the community. These data may be used to develop a COVID-19 risk assessment system that is tailored toward a specific group of people.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The aim of this study was to identify the most common symptoms reported by patients with COVID-19, as well as the order of symptom appearance, by examining tweets in Arabic.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We searched Twitter posts in Arabic for personal reports of COVID-19 symptoms from March 1 to May 27, 2020. We identified 463 Arabic users who had tweeted about testing positive for COVID-19 and extracted the symptoms they associated with the disease. Furthermore, we asked them directly via personal messaging to rank the appearance of the first 3 symptoms they had experienced immediately before (or after) their COVID-19 diagnosis. Finally, we tracked their Twitter timeline to identify additional symptoms that were mentioned within \u00b15 days from the day of the first tweet on their COVID-19 diagnosis. In total, 270 COVID-19 self-reports were collected, and symptoms were (at least partially) ranked.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The collected self-reports contained 893 symptoms from 201 (74%) male and 69 (26%) female Twitter users. The majority (n=270, 82%) of the tracked users were living in Saudi Arabia (n=125, 46%) and Kuwait (n=98, 36%). Furthermore, 13% (n=36) of the collected reports were from asymptomatic individuals. Of the 234 users with symptoms, 66% (n=180) provided a chronological order of appearance for at least 3 symptoms. Fever (n=139, 59%), headache (n=101, 43%), and anosmia (n=91, 39%) were the top 3 symptoms mentioned in the self-reports. Additionally, 28% (n=65) reported that their COVID-19 experience started with a fever, 15% (n=34) with a headache, and 12% (n=28) with anosmia. Of the 110 symptomatic cases from Saudi Arabia, the most common 3 symptoms were fever (n=65, 59%), anosmia (n=46, 42%), and headache (n=42, 38%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study identified the most common symptoms of COVID-19 from tweets in Arabic. These symptoms can be further analyzed in clinical settings and may be incorporated into a real-time COVID-19 risk estimator.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/21329","type":"journal-article","created":{"date-parts":[[2020,10,26]],"date-time":"2020-10-26T21:57:23Z","timestamp":1603749443000},"page":"e21329","source":"Crossref","is-referenced-by-count":20,"title":["Identifying and Ranking Common COVID-19 Symptoms From Tweets in Arabic: Content Analysis"],"prefix":"10.2196","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5560-1116","authenticated-orcid":false,"given":"Eisa","family":"Alanazi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1525-4500","authenticated-orcid":false,"given":"Abdulaziz","family":"Alashaikh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5090-687X","authenticated-orcid":false,"given":"Sarah","family":"Alqurashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1861-0360","authenticated-orcid":false,"given":"Aued","family":"Alanazi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2020,11,18]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487709"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/icbda.2018.8367652"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.26599\/bdma.2019.9020012"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3390\/app10041398"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocaa116"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1101\/2020.04.19.20069948"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.2105\/AJPH.2016.303512"},{"key":"ref8","unstructured":"Ministry of Health Saudi ArabiaThe Central Appointment System (Mawid)Ministry of Health E-Services2020-05-27https:\/\/www.moh.gov.sa\/en\/eServices\/Pages\/cassystem.aspx"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.2196\/19509"},{"key":"ref10","unstructured":"Tweetgen20202020-06-02https:\/\/www.tweetgen.com\/"},{"key":"ref11","unstructured":"NugaliNSaudi Arabia\u2019s active mass testing contains COVID-19 spreadArab News202004212020-05-25https:\/\/www.arabnews.com\/node\/1662856\/saudi-arabia"},{"key":"ref12","unstructured":"Leading countries based on number of Twitter users as of April 2020Statista20202020-05-28https:\/\/www.statista.com\/statistics\/242606\/number-of-active-twitter-users-in-selected-countries\/"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1142\/9789814749411_0043"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0061373"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1080\/23744235.2020.1772992"},{"key":"ref16","unstructured":"WorldometerCOVID-19 Pandemic20202020-06-09https:\/\/www.worldometers.info\/coronavirus\/"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/snams.2019.8931720"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.2775"},{"key":"ref19","unstructured":"Saudi Arabic Coronavirus disease (COVID-19) situationKAPSARC20202020-07-22https:\/\/datasource.kapsarc.org\/explore\/dataset\/saudi-arabia-coronavirus-disease-covid-19-situation\/information"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.3617"},{"key":"ref21","unstructured":"TwitterTwitter Developer Policy2020-04-15https:\/\/developer.twitter.com\/en\/developer-terms\/policy"}],"container-title":["Journal of Medical Internet Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jmir.org\/2020\/11\/e21329","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:29:06Z","timestamp":1782840546000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.jmir.org\/2020\/11\/e21329\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,18]]},"references-count":21,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020]]}},"URL":"https:\/\/doi.org\/10.2196\/21329","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.06.10.20127225","asserted-by":"object"},{"id-type":"doi","id":"10.2196\/preprints.21329","asserted-by":"object"}]},"ISSN":["1438-8871"],"issn-type":[{"value":"1438-8871","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,18]]}}}