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Background <p>Patient perceptions influence the success of bariatric surgery and pharmacologic weight loss therapies, yet many concerns never reach providers during clinic visits. Reddit, a popular online community, offers a window to explore these concerns outside clinical settings. We leveraged natural language processing (NLP) to compare user-submitted content across communities in surgical and non-surgical weight loss communities on Reddit.</p> Methods <p>We extracted the top 500 posts and all comments from three Reddit communities: /r/GastricBypass (/r/GB), /r/GastricSleeve (/r/GS), and /r/Ozempic. NLP libraries were used to quantify sentiment and subjectivity. Latent Dirichlet Allocation (LDA) was used to perform topic modeling and identify themes. Significance was obtained with Kruskal–Wallis and chi-square (<i>χ</i>2) tests. A machine learning classifier categorized posts as pre- or post-intervention.</p> Results <p>1500 posts and 45,972 comments were analyzed. Sentiment was largely neutral/positive (0.26 ± 0.35) and moderately subjective (0.48 ± 0.33). Comment sentiment was most positive in /r/GS and least in /r/GB (0.30 ± 0.33 vs 0.20 ± 0.31, respectively, <i>P</i> &lt; 0.001). Post sentiment did not vary (<i>P</i> = 0.246). Five main themes emerged: eating behaviors/diet, weight loss progress, medication/medical issues, adverse effects, and support. Pain was the biggest adverse effect among surgical subreddits (24.3% within /r/GB and 16.8% /r/GS) compared to gastrointestinal (20.8%) and psychological (7.7%) symptoms in /r/Ozempic (<i>P</i> &lt; 0.001). Most content was post-intervention (85.0%), and pre-intervention posts showed higher levels of anxiety and depression (4.0% vs 0.9%, <i>P</i> = 0.049).</p> Conclusions <p>Our findings reveal distinct patient concerns across weight loss intervention. NLP surveillance of online communities can help clinicians identify latent concerns, tailor counseling, and manage expectations.</p>

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Pre-operative concerns and post-operative satisfaction: comparing attitudes toward bariatric surgery and medication intervention for weight loss on Reddit

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • Gabriel A. del Carmen,
  • Dhara Patel,
  • Natalie Vu,
  • Alexander Tran,
  • Jessica A. Zaman

摘要

Background

Patient perceptions influence the success of bariatric surgery and pharmacologic weight loss therapies, yet many concerns never reach providers during clinic visits. Reddit, a popular online community, offers a window to explore these concerns outside clinical settings. We leveraged natural language processing (NLP) to compare user-submitted content across communities in surgical and non-surgical weight loss communities on Reddit.

Methods

We extracted the top 500 posts and all comments from three Reddit communities: /r/GastricBypass (/r/GB), /r/GastricSleeve (/r/GS), and /r/Ozempic. NLP libraries were used to quantify sentiment and subjectivity. Latent Dirichlet Allocation (LDA) was used to perform topic modeling and identify themes. Significance was obtained with Kruskal–Wallis and chi-square (χ2) tests. A machine learning classifier categorized posts as pre- or post-intervention.

Results

1500 posts and 45,972 comments were analyzed. Sentiment was largely neutral/positive (0.26 ± 0.35) and moderately subjective (0.48 ± 0.33). Comment sentiment was most positive in /r/GS and least in /r/GB (0.30 ± 0.33 vs 0.20 ± 0.31, respectively, P < 0.001). Post sentiment did not vary (P = 0.246). Five main themes emerged: eating behaviors/diet, weight loss progress, medication/medical issues, adverse effects, and support. Pain was the biggest adverse effect among surgical subreddits (24.3% within /r/GB and 16.8% /r/GS) compared to gastrointestinal (20.8%) and psychological (7.7%) symptoms in /r/Ozempic (P < 0.001). Most content was post-intervention (85.0%), and pre-intervention posts showed higher levels of anxiety and depression (4.0% vs 0.9%, P = 0.049).

Conclusions

Our findings reveal distinct patient concerns across weight loss intervention. NLP surveillance of online communities can help clinicians identify latent concerns, tailor counseling, and manage expectations.