{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:02:39Z","timestamp":1782846159184,"version":"3.54.5"},"reference-count":43,"publisher":"JMIR Publications Inc.","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Public Health Surveill"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The increasing volume of health-related social media activity, where users connect, collaborate, and engage, has increased the significance of analyzing how people use health-related social media.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The aim of this study was to classify the content (eg, posts that share experiences and seek support) of users who write health-related social media posts and study the effect of user demographics on post content.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed two different types of health-related social media: (1) health-related online forums\u2014WebMD and DailyStrength\u2014and (2) general online social networks\u2014Twitter and Google+. We identified several categories of post content and built classifiers to automatically detect these categories. These classifiers were used to study the distribution of categories for various demographic groups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We achieved an accuracy of at least 84% and a balanced accuracy of at least 0.81 for half of the post content categories in our experiments. In addition, 70.04% (4741\/6769) of posts by male WebMD users asked for advice, and male users\u2019 WebMD posts were more likely to ask for medical advice than female users\u2019 posts. The majority of posts on DailyStrength shared experiences, regardless of the gender, age group, or location of their authors. Furthermore, health-related posts on Twitter and Google+ were used to share experiences less frequently than posts on WebMD and DailyStrength.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We studied and analyzed the content of health-related social media posts. Our results can guide health advocates and researchers to better target patient populations based on the application type. Given a research question or an outreach goal, our results can be used to choose the best online forums to answer the question or disseminate a message.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/14952","type":"journal-article","created":{"date-parts":[[2020,1,27]],"date-time":"2020-01-27T22:49:08Z","timestamp":1580165348000},"page":"e14952","source":"Crossref","is-referenced-by-count":14,"title":["Classification of Health-Related Social Media Posts: Evaluation of Post Content\u2013Classifier Models and Analysis of User Demographics"],"prefix":"10.2196","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5590-0274","authenticated-orcid":false,"given":"Ryan","family":"Rivas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5203-4355","authenticated-orcid":false,"given":"Shouq A","family":"Sadah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0495-0754","authenticated-orcid":false,"given":"Yuhang","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8679-4988","authenticated-orcid":false,"given":"Vagelis","family":"Hristidis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2020,4,1]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.1933"},{"issue":"11","key":"ref2","first-page":"45","volume":"87","author":"Kane, GC","year":"2009","journal-title":"Harv Bus Rev"},{"issue":"2","key":"ref3","first-page":"1","volume":"7","author":"Hackworth, BA","year":"2011","journal-title":"Acad Health Care Manag J"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2014.03.006"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1177\/0956797614557867"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1002\/pra2.2017.14505401179"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.647"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.3765"},{"issue":"1","key":"ref9","volume":"24","author":"Lopes, CT","year":"2019","journal-title":"Inf Res"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0131809"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.1551"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.4308"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.5327"},{"key":"ref14","unstructured":"SadilekAKautzHSilenzioVModeling Spread of Disease From Social InteractionsProceedings of the Sixth International AAAI Conference on Weblogs and Social Media2012ICWSM'12June 4-7, 2012Dublin, Ireland"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2013.08.011"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocu041"},{"key":"ref17","unstructured":"MisloveALehmannSAhnYYOnnelaJRosenquistJNUnderstanding the Demographics of Twitter UsersProceedings of the 5th international AAAI conference on weblogs and social media2011ICWSM'11July 17-21, 2011Barcelona, Spain"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.5369"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/SCOPES.2016.7955858"},{"key":"ref20","unstructured":"RxList - The Internet Drug Index for Prescription Drug Information, Interactions, and Side Effects2015-02-02http:\/\/www.rxlist.com\/script\/main\/hp.asp"},{"key":"ref21","unstructured":"Twitter Developer2015-06-14https:\/\/developer.twitter.com\/"},{"key":"ref22","unstructured":"2015-02-02Google+ API | Google+ Platform for Web. | Google Developershttps:\/\/www.webcitation.org\/78S7b05G0"},{"key":"ref23","unstructured":"HedleyJjsoup Java HTML Parser, with best of DOM, CSS, and jquery2015-02-02http:\/\/jsoup.org\/"},{"key":"ref24","unstructured":"Twitter2016-05-19https:\/\/twitter.com\/"},{"key":"ref25","unstructured":"Google Plus2016-05-19https:\/\/plus.google.com\/"},{"key":"ref26","unstructured":"2015-02-02DailyStrength: Support Groups.http:\/\/www.dailystrength.org\/support-groups"},{"key":"ref27","unstructured":"WebMD - Better Information. 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