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Parisa Momeni

Publications and source records attributed to Parisa Momeni.

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

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.

cs.SI

Toward satisfactory public accessibility: A crowdsourcing approach through online reviews to inclusive urban design

As urban populations grow, the need for accessible urban design has become urgent. Traditional survey methods for assessing public perceptions of accessibility are often limited in scope. Crowdsourcing via online reviews offers a valuable alternative to understanding public perceptions, and advancements in large language models can facilitate their use. This study uses Google Maps reviews across the United States and fine-tunes Llama 3 model with the Low-Rank Adaptation technique to analyze public sentiment on accessibility. At the POI level, most categories -- restaurants, retail, hotels, and healthcare -- show negative sentiments. Socio-spatial analysis reveals that areas with higher proportions of white residents and greater socioeconomic status report more positive sentiment, while areas with more elderly, highly-educated residents exhibit more negative sentiment. Interestingly, no clear link is found between the presence of disabilities and public sentiments. Overall, this study highlights the potential of crowdsourcing for identifying accessibility challenges and providing insights for urban planners.

cs.SI