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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.

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.

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Purpose: Interstitial cystitis/bladder pain syndrome is a debilitating chronic condition that disproportionately affects women at a ratio of 5:1. We sought to capture women's experiences with interstitial cystitis/bladder pain syndrome by conducting a large-scale digital ethnographic analysis of anonymous posts on Internet forums. Materials and Methods: Online posts were identified using condition-specific keywords and data mining extraction services. Once posts were identified, a random sample of 200 online posts was coded and analyzed by hand using qualitative methods. A Latent Dirichlet Allocation probabilistic topic model was applied to the complete dataset to substantiate the qualitative analysis and allow for further thematic discovery. Results: A total of 6,842 posts written by 3,902 unique users from 224 websites were identified. There was a significant overlap between the hand coding and Latent Dirichlet Allocation themes. Our analysis yielded the following themes: online community engagement, triggers and disease etiologies, medical comorbidities, quality of life impact, patient experience with medical care, and alternative therapies and self-management strategies. Additionally, our population appeared to have a high burden of nonurological associated syndromes. We identified barriers to patient-centered care and found that online peer support was important for women. Conclusions: Our digital ethnographic analysis is a novel application of qualitative methods using online sources. Social media analytics appears to capture a broader patient population than that typically included in clinic-based qualitative studies, such as patient interviews and focus groups. Understanding patient behaviors and concerns are important to guide strategies for improving care and the overall experience with this difficult-to-treat condition.