Evidence map›Paper›PMID 41734897›Full record

ArticleOnline journal of public health informatics2026

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

Parisa Momeni, Gabriel Laverghetta, Jay Ligatti, Lingyao Li

Abstract read
In one paragraph

Article in Online journal of public health informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Parisa Momeni *Bellini College of Artificial Intelligence, Cybersecurity and Computing, University of South Florida, Tampa, FL, United States.ORCID https://orcid.org/0009-0003-7757-5670
Gabriel Laverghetta *Bellini College of Artificial Intelligence, Cybersecurity and Computing, University of South Florida, Tampa, FL, United States.ORCID https://orcid.org/0009-0007-7925-7587
Jay LigattiBellini College of Artificial Intelligence, Cybersecurity and Computing, University of South Florida, Tampa, FL, United States.ORCID https://orcid.org/0000-0001-9335-0517
Lingyao LiSchool of Information, University of South Florida, Tampa, FL, United States.ORCID https://orcid.org/0000-0001-5888-8311

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUser experience has a significant impact on pharmaceutical drug effectiveness. Social media platforms like X (formerly Twitter) have become prominent spaces where individuals share their medication-related experiences, especially with widely marketed drugs such as semaglutide. Despite the large volume of conversation, a comprehensive understanding of how various user subpopulations engage with semaglutide-related discussions remains underdeveloped.

objectiveThis study aims to explore how semaglutide is perceived and discussed across different X user groups. Within these user groups, we investigate (1) the evolution of sentiment patterns toward semaglutide and (2) the evolution and prevalence of semaglutide-related discussion topics.

methodsWe prepared a dataset consisting of 859,751 X posts (tweets) pertaining to semaglutide, along with related metadata, that were posted between July 2021 and April 2024. We apply sentiment analysis and topic modeling to the collected posts and analyze the sentiment patterns and topics within specific user subpopulations and time periods.

resultsOur analysis reveals a mean sentiment score of -0.24 (SD 0.669) across all posts, with all user subpopulations experiencing a decline in sentiment during the study period. User discussions focus on semaglutide's applications in weight loss and potential side effects, along with economic factors and celebrity/political influence. We also uncover differences in sentiment and discussion topics across user subpopulations. Notably, organizational accounts consistently express less negative sentiment (mean -0.04, SD 0.542) than individuals (mean -0.28, SD 0.605), with a statistically significant difference (P<.001), particularly in discussions related to drug efficacy and regulatory concerns. Interrupted time-series analysis shows a marked decrease in sentiment during the November 2022-January 2023 period, coinciding with regulatory announcements about potential adverse effects. In addition, we observe gender-based variations, such as a greater prevalence of discussions involving celebrities and politicians within female user posts (8368/39,786, 21%) compared to male user posts (8087/46,133, 17.5%), and male users expressing more positive sentiment.

conclusionsThis study helps advance the understanding of how diverse user groups perceive and discuss widely marketed drugs like semaglutide. Although we observe a general negativity, there are nuanced differences among the subpopulations. Our results offer valuable implications for health communication strategies and pharmacovigilance.

Indexed as

public healthsemaglutidesentiment analysissocial mediatopic modelinguser experience

Identifiers

PMID41734897
PMCPMC12976598

What Socratic holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.