Evidence map›Paper›PMID 41554176›Full record

ArticleThe western journal of emergency medicine2025

Mapping Five Years of #FOAMed: Trends, Engagement, and Shifting Topics on Twitter/X.

Ertuğ Günsoy, Ahmet Aykut, Cem Yildirim, Mehmet Veysel Öncül

Abstract read
In one paragraph

Article in The western journal of emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ertuğ GünsoyUniversity of Health Sciences, Van Education and Research Hospital, Department of Emergency Medicine, Van, Türkiye.
Ahmet AykutUniversity of Health Sciences, Van Education and Research Hospital, Department of Emergency Medicine, Van, Türkiye.
Cem YildirimUniversity of Health Sciences, Van Education and Research Hospital, Department of Emergency Medicine, Van, Türkiye.
Mehmet Veysel ÖncülUniversity of Health Sciences, Van Education and Research Hospital, Department of Emergency Medicine, Van, Türkiye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionFree Open Access Medical Education (FOAMed) has emerged as a prominent component of online medical communication, with X (formerly Twitter) serving as an active hub for professional exchange among clinicians. Despite its reach and influence, few longitudinal studies have examined how FOAMed content and engagement patterns evolve over time. In this study we aimed to analyze thematic shifts and user interaction trends in #FOAMed tweets over a five-year period.

methodsWe conducted a retrospective bibliometric and natural language processing (NLP) study of 6,000 high-engagement, English-language tweets tagged with #FOAMed, posted between January 1, 2020-December 31, 2024. Each month, the 100 tweets were selected from Twitter's "Top" tab and manually curated. We used latent Dirichlet allocation (LDA) to identify thematic clusters. Hashtag usage and engagement metrics were assessed using descriptive statistics and linear regression.

resultsWe identified 10 distinct topics were identified through LDA modeling: point-of-care ultrasound (POCUS) education; neuro-radiology, cardiology-electrocardiogram (ECG); nephrology; and intensive care unit; ultrasound; prehospital/policy; webinars and learning; resuscitation scenarios; pediatric imaging; medical student education; and critical care and publications. Topic prevalence shifted over time: Early tweets focused on COVID-19 and critical care, while later years showed increasing attention to prehospital care, diagnostics, and POCUS. Mean tweet engagement peaked in 2023 (236.9 ± 914.6). Notably, hashtags such as #POCUS and #MedEd showed substantial increases in both usage and engagement, with #MedEd reaching a peak mean engagement of 287.7. In contrast, COVID-19 declined steadily, both in frequency (from 126 tweets in 2020 to just six in 2023) and in engagement (mean: 67.1 → 18.5). Spearman correlation analysis revealed that hashtag count had a weak but statistically significant correlation with engagement (ρ = 0.047, P < .001), suggesting that content quality, rather than volume, was the primary driver of visibility.

conclusionFOAMed discourse on Twitter/X remains dynamic, responsive to clinical priorities and shaped by peer interaction. Natural language processing and topic modeling are valuable tools to uncover longitudinal trends in digital medical education, reinforcing Twitter/X's role in informal, real-time learning communities.

Indexed as

Education, MedicalSocial MediaBibliometricsCOVID-19HumansNatural Language ProcessingRetrospective Studies

Identifiers

PMID41554176
PMCPMC12815566

What OpenQuestion 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 OpenQuestion graph.