{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T13:59:12Z","timestamp":1790776752422,"version":"4.1.0"},"reference-count":70,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Interventions are needed to address the lack of pre-exposure prophylaxis (PrEP) awareness and mitigate barriers related to PrEP use. One such intervention modality is social media, as PrEP awareness and communication on issues, such as access and cost, are easily addressed via clear social media messages on platforms for PrEP-eligible people, and especially young people, who use social media more frequently.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This descriptive study seeks to extend understanding of PrEP awareness and usage by examining PrEP-related communication across 3 popular social media platforms (Facebook [Meta Platforms, Inc], Instagram [Meta Platforms, Inc], and Twitter [rebranded as X]) and identifying message and source characteristics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In February 2023, we used CrowdTangle (a public insights tool owned by Facebook, now known as Meta) to gather a total of 39,790 Facebook posts and 5628 Instagram posts. We also used Twitter\u2019s public API to collect 14,061 Twitter posts during the same time frame. Of these, we drew a random sample of social media posts from each platform (1000 posts from Facebook, 1000 posts from Instagram, and 811 posts from Twitter) in February 2023 and analyzed them using a quantitative content analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our findings showed some differences in the types of text-based content appearing on each platform. We also uncovered similar patterns across all three platforms: (1) definitions of and indications for PrEP were the most common type of text-based content in posts; (2) information about PrEP appearing in social media posts drew from websites other than traditional sources such as public health organizations, health care providers, and government agencies; and (3) men who have sex with men represented the most frequently mentioned target population. Although our study did not detect a large presence of theory-based concepts from behavior change theory, such as the integrative model of behavioral prediction, across all platforms, attitude emerged most frequently, followed by self-efficacy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These findings shed light on the PrEP-related content that may reach and shape the perceptions of young people who could benefit from PrEP use. Such insights can guide the design of future social media\u2013based messages targeting the most influential beliefs to strengthen HIV prevention efforts.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/87607","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T16:40:08Z","timestamp":1786380008000},"page":"e87607-e87607","source":"Crossref","is-referenced-by-count":0,"title":["Pre-Exposure Prophylaxis\u2013Related Messages Across Facebook, Instagram, and Twitter (Rebranded X): Content Analysis"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8011-120X","authenticated-orcid":false,"given":"Lourdes S","family":"Martinez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1019-8546","authenticated-orcid":false,"given":"Nicole","family":"Crocker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5227-6465","authenticated-orcid":false,"given":"Sharanya","family":"Akkone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2683-471X","authenticated-orcid":false,"given":"Shravani","family":"Hariprasad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2590-7907","authenticated-orcid":false,"given":"Rebecca","family":"Houghton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1690-5352","authenticated-orcid":false,"given":"Eric R","family":"Walsh-Buhi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"R1","unstructured":"U.S. Statistics. 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