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Objective: To characterize the decision-making process and illness experience of women with pelvic organ prolapse (POP) using large-scale social media analysis., Methods: Digital ethnographic analysis of online posts identified through data mining was performed. Grounded theory methodology was applied to 200 posts via traditional hand coding. To supplement our qualitative approach, we applied a Latent Dirichlet Allocation probabilistic topic modeling process to review the entire data set of identified posts to ensure thematic saturation., Results: There were 3451 posts by 2088 unique users from 117 websites worldwide that were identified via social media data mining. We found that the anonymity of online forums allowed for information and support exchange among women with POP. Our analysis revealed that the exchange of online information aids in the decision-making process and, in some instances, appears to be the primary source of information. There was confusion about the anatomical and surgical complexities of prolapse. Our study also identified misconceptions, perceived risk factors, prevention methods, and management recommendations that were discussed online., Conclusions: This large-scale online community-based analysis demonstrated the utility of social media to better understand women's experiences with POP. Thematic findings highlighted essential concerns and challenges involved in the surgical decision-making process and the understating of the anatomical complexity of sector defects, specifically to cystocele, rectocele, State specific defects., (Copyright © 2020 American Urogynecologic Society. All rights reserved.)

Objective: Few studies have examined ankylosing spondylitis (AS) patients' concerns about and perceptions of biologic therapies, apart from traditional surveys. In this study, we used social media data to examine the knowledge, attitudes, and beliefs of AS patients regarding biologic therapies., Methods: We collected posts published on 601 social media sites between January 1, 2016 and April 26, 2017. In each post, both an AS keyword and a biologic were mentioned. To explore themes within the collection of posts in an unsupervised manner, a latent Dirichlet allocation topic model was fit to the data set. Each discovered topic was represented as a discrete distribution over the words in the collection, similar to a word cloud. The topics were manually reviewed to identify themes, which were confirmed using thematic data analysis., Results: We examined 27,416 social media posts and identified 112 themes. The majority of themes (n = 67 [60%]) focused on discussions related to AS treatment. Other themes, including the psychological impact of AS, reporting of medical literature, and AS disease consequences, accounted for the remaining 40% (n = 45). In discussions regarding AS treatment, most topics involved biologics, and most subthemes involved side effects (e.g., fatigue, allergic reactions), biologic treatment attributes (e.g., dosing, frequency), and concerns about use of biologics (e.g., increased cancer risk). Additional implicit patient needs (e.g., support) were identified using qualitative analyses., Conclusion: Social media revealed a dynamic range of themes governing AS patients' experience with and choice of biologic agents. The complexity of selecting biologics from among many such agents and navigating their risk/benefit profiles suggests the merit of creating online tools tailored to support patients' decision-making with regard to biologic therapies for AS., (© 2018, American College of Rheumatology.)

Background: Current qualitative literature about the experiences of women dealing with urinary tract infections (UTIs) is limited to patients recruited from tertiary centers and medical clinics. However, traditional focus groups and interviews may limit what patients share. Using digital ethnography, we analyzed free-range conversations of an online community. Objective: This study aimed to investigate and characterize the patient perspectives of women dealing with UTIs using digital ethnography. Methods: A data-mining service was used to identify online posts. A thematic analysis was conducted on a subset of the identified posts. Additionally, a latent Dirichlet allocation (LDA) probabilistic topic modeling method was applied to review the entire data set using a semiautomatic approach. Each identified topic was generated as a discrete distribution over the words in the collection, which can be thought of as a word cloud. We also performed a thematic analysis of the word cloud topic model results. Results: A total of 83,589 posts by 53,460 users from 859 websites were identified. Our hand-coding inductive analysis yielded the following 7 themes: quality-of-life impact, knowledge acquisition, support of the online community, health care utilization, risk factors and prevention, antibiotic treatment, and alternative therapies. Using the LDA topic model method, 105 themes were identified and consolidated into 9 categories. Of the LDA-derived themes, 25.7% (27/105) were related to online community support, and 22% (23/105) focused on UTI risk factors and prevention strategies. Conclusions: Our large-scale social media analysis supports the importance and reproducibility of using online data to comprehend women's UTI experience. This inductive thematic analysis highlights patient behavior, self-empowerment, and online media utilization by women to address their health concerns in a safe, anonymous way. J Med Internet Res 2022;24(1):e26781 doi:10.2196/26781 [ABSTRACT FROM AUTHOR]