{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T11:54:42Z","timestamp":1791892482676,"version":"4.3.4"},"reference-count":25,"publisher":"JMIR Publications Inc.","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"\n Background<\/jats:title>\n The #MeToo movement sparked an international debate on the sexual harassment, abuse, and assault and has taken many directions since its inception in October of 2017. Much of the early conversation took place on public social media sites such as Twitter, where the hashtag movement began.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n The aim of this study is to document, characterize, and quantify early public discourse and conversation of the #MeToo movement from Twitter data in the United States. We focus on posts with public first-person revelations of sexual assault\/abuse and early life experiences of such events.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We purchased full tweets and associated metadata from the Twitter Premium application programming interface between October 14 and 21, 2017 (ie, the first week of the movement). We examined the content of novel English language tweets with the phrase \u201cMeToo\u201d from within the United States (N=11,935). We used machine learning methods, least absolute shrinkage and selection operator regression, and support vector machine models to summarize and classify the content of individual tweets with revelations of sexual assault and abuse and early life experiences of sexual assault and abuse.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n We found that the most predictive words created a vivid archetype of the revelations of sexual assault and abuse. We then estimated that in the first week of the movement, 11% of novel English language tweets with the words \u201cMeToo\u201d revealed details about the poster\u2019s experience of sexual assault or abuse and 5.8% revealed early life experiences of such events. We examined the demographic composition of posters of sexual assault and abuse and found that white women aged 25-50 years were overrepresented in terms of their representation on Twitter. Furthermore, we found that the mass sharing of personal experiences of sexual assault and abuse had a large reach, where 6 to 34 million Twitter users may have seen such first-person revelations from someone they followed in the first week of the movement.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n These data illustrate that revelations shared went beyond acknowledgement of having experienced sexual harassment and often included vivid and traumatic descriptions of early life experiences of assault and abuse. These findings and methods underscore the value of content analysis, supported by novel machine learning methods, to improve our understanding of how widespread the revelations were, which likely amplified the spread and saliency of the #MeToo movement.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/13837","type":"journal-article","created":{"date-parts":[[2019,7,7]],"date-time":"2019-07-07T17:57:53Z","timestamp":1562522273000},"page":"e13837","source":"Crossref","is-referenced-by-count":48,"title":["The #MeToo Movement in the United States: Text Analysis of Early Twitter Conversations"],"prefix":"10.2196","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4557-7156","authenticated-orcid":false,"given":"Sepideh","family":"Modrek","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5022-2697","authenticated-orcid":false,"given":"Bozhidar","family":"Chakalov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2019,9,3]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jadohealth.2014.10.270"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM.2017.8217766"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.3247"},{"key":"ref4","unstructured":"PaulMJDredzeMYou Are What You Tweet: Analyzing Twitter for Public Health201107175th Int AAAI Conf Weblogs Soc Media ICWSMJuly 17, 2011Barcelona, 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Journal"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1080\/09589236.2016.1211511"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1177\/1350506818765318"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1177\/1461444816663485"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1111\/cpsp.12004"},{"key":"ref16","unstructured":"HouryDSmithSChenJBasileKGilbertLMerrickMPatelNWallingMJainACenters for Disease Control and Prevention201705012019-02-25The National Intimate Partner and Sexual Violence Survey (NISVS) | 2010-2012 State Report \n https:\/\/www.cdc.gov\/violenceprevention\/pdf\/NISVS-StateReportBook.pdf"},{"key":"ref17","unstructured":"Rape Culture Pyramid2019-02-2611th Principle Consent \n https:\/\/www.11thprincipleconsent.org\/consent-propaganda\/rape-culture-pyramid\/"},{"key":"ref18","author":"James, G","year":"2013","journal-title":"Springer"},{"key":"ref19","unstructured":"GentzkowMKellyBTaddyMThe National Bureau of Economic Research201703Cambridge, MAText as Data \n https:\/\/www.nber.org\/papers\/w23276"},{"key":"ref20","unstructured":"KowalczykASVM Tutorial201411232018-08-01SVM Tutorial: How to classify text in R \n https:\/\/www.svm-tutorial.com\/2014\/11\/svm-classify-text-r\/"},{"key":"ref21","unstructured":"Demographics Pro2019-01-25https:\/\/www.demographicspro.com\/"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2015.08.023"},{"key":"ref23","unstructured":"RennisonCallie MarieU.S. Department of Justice200208012019-07-29Washington, DCU.S. Department of JusticeRape and Sexual Assault: Reporting to Police and Medical Attention, 1992-2000 \n https:\/\/www.bjs.gov\/content\/pub\/pdf\/rsarp00.pdf"},{"key":"ref24","unstructured":"SiniRBBC201712272019-02-25#MeToo, #TakeAKnee and #Covfefe: Hashtags that dominated in 2017 \n https:\/\/www.bbc.com\/news\/world-42251490"},{"key":"ref25","unstructured":"LaMotteSCNN Health201710192019-02-27For some, #MeToo sexual assault stories trigger trauma not empowerment \n https:\/\/www.cnn.com\/2017\/10\/19\/health\/me-too-sexual-assault-stories-trigger-trauma\/index.html"}],"container-title":["Journal of Medical Internet Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jmir.org\/2019\/9\/e13837","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:03:15Z","timestamp":1782842595000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jmir.org\/2019\/9\/e13837\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,3]]},"references-count":25,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2019]]}},"URL":"https:\/\/doi.org\/10.2196\/13837","relation":{"has-preprint":[{"id-type":"doi","id":"10.2196\/preprints.13837","asserted-by":"object"}]},"ISSN":["1438-8871"],"issn-type":[{"value":"1438-8871","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,3]]}}}