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arXiv · 2505.18432

Topic and Sentiment Trends in Semaglutide Discussions on X: Subpopulation-Based Longitudinal Analysis

Parisa Momeni·Gabriel Laverghetta·Jay Ligatti·Lingyao Li
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Abstract

Background: User experience strongly influences pharmaceutical drug effectiveness. Social media platforms like X have become major spaces where people share medication-related experiences, especially for widely marketed drugs such as semaglutide. Despite high activity online, how different user groups engage in semaglutide discussions remains unclear. Objective: This study examines how semaglutide is perceived and discussed across X user groups by analyzing (1) changes in sentiment over time and (2) key discussion topics. Methods: We collected 859,751 posts about semaglutide from July 2021 to April 2024, along with metadata. We performed sentiment analysis and topic modeling to evaluate patterns across user subpopulations and time periods. Results: The overall mean sentiment was -0.24, with all groups showing declines over time. Discussions focused on weight loss, side effects, costs, and celebrity or political influence. Organizational accounts expressed less negative sentiment (mean = -0.04) than individuals (mean = -0.28), a statistically significant difference (P < .001). An interrupted time-series analysis showed a sentiment drop between Nov 2022 and Jan 2023, coinciding with regulatory announcements. We also found gender differences: posts by female users contained more discussions of celebrities and politicians (21 percent) compared to male users (17 percent), while male users expressed more positive sentiment. Conclusions: This study highlights how diverse user groups perceive and discuss semaglutide. Although sentiment was broadly negative, important differences emerged across subpopulations. These findings have implications for health communication and pharmacovigilance.

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Parisa Momeni, Gabriel Laverghetta, Jay Ligatti, Lingyao Li. 2025-05-23. Topic and Sentiment Trends in Semaglutide Discussions on X: Subpopulation-Based Longitudinal Analysis. https://arxiv.org/abs/2505.18432

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