{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T18:26:24Z","timestamp":1790706384653,"version":"4.1.0"},"reference-count":70,"publisher":"JMIR Publications Inc.","issue":"12","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>Low back pain (LBP) remains the leading cause of disability worldwide. A better understanding of the beliefs regarding LBP and impact of LBP on the individual is important in order to improve outcomes. Although personal experiences of LBP have traditionally been explored through qualitative studies, social media allows access to data from a large, heterogonous, and geographically distributed population, which is not possible using traditional qualitative or quantitative methods. As data on social media sites are collected in an unsolicited manner, individuals are more likely to express their views and emotions freely and in an unconstrained manner as compared to traditional data collection methods. Thus, content analysis of social media provides a novel approach to understanding how problems such as LBP are perceived by those who experience it and its impact.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The objective of this study was to identify contextual variables of the LBP experience from a first-person perspective to provide insights into individuals\u2019 beliefs and perceptions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed 896,867 cleaned tweets about  LBP between January 1, 2014, and December 31, 2018. We tested and compared latent Dirichlet allocation (LDA), Dirichlet multinomial mixture (DMM), GPU-DMM, biterm topic model, and nonnegative matrix factorization for identifying topics associated with tweets. A coherence score was determined to identify the best model. Two domain experts independently performed qualitative content analysis of the topics with the strongest coherence score and grouped them into contextual categories. The experts met and reconciled any differences and developed the final labels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>LDA outperformed all other algorithms, resulting in the highest coherence score. The best model was LDA with 60 topics, with a coherence score of 0.562. The 60 topics were grouped into 19 contextual categories. \u201cEmotion and beliefs\u201d had the largest proportion of total tweets (157,563\/896,867, 17.6%), followed by \u201cphysical activity\u201d (124,251\/896,867, 13.85%) and \u201cdaily life\u201d (80,730\/896,867, 9%), while \u201cfood and drink,\u201d \u201cweather,\u201d and \u201cnot being understood\u201d had the smallest proportions (11,551\/896,867, 1.29%; 10,109\/896,867, 1.13%; and 9180\/896,867, 1.02%, respectively). Of the 11 topics within \u201cemotion and beliefs,\u201d 113,562\/157,563 (72%) had negative sentiment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The content analysis of tweets in the area of LBP identified common themes that are consistent with findings from conventional qualitative studies but provide a more granular view of individuals\u2019 perspectives related to LBP. This understanding has the potential to assist with developing more effective and personalized models of care to improve outcomes in those with LBP.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/26093","type":"journal-article","created":{"date-parts":[[2021,11,21]],"date-time":"2021-11-21T20:59:05Z","timestamp":1637528345000},"page":"e26093","source":"Crossref","is-referenced-by-count":10,"title":["Investigating Individuals\u2019 Perceptions Regarding the Context Around the Low Back Pain Experience: Topic Modeling Analysis of Twitter Data"],"prefix":"10.2196","volume":"23","author":[{"name":"Robert","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9922-1214","authenticated-orcid":false,"given":"Pari","family":"Delir 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