ArticleHealthcare (Basel, Switzerland)2023
Topic Modeling Analysis of Diabetes-Related Health Information during the Coronavirus Disease Pandemic.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- The quality and scope of health information on online drug platforms: a topic modelling and expert evaluation study of a Polish-language forum.Harm reduction journal · 2026Article
- Analyzing Online Search Trends for Kidney, Prostate, and Bladder Cancers in China: Infodemiology Study Using Baidu Search Data (2011-2023).JMIR cancer · 2025Article
- The Utilization of Natural Language Processing for Analyzing Social Media Data in Nursing Research: A Scoping Review.Journal of nursing management · 2024Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to provide diabetes-related health information by analyzing queries posted in the diabetes-related online community required during the COVID-19 pandemic. A total of 9156 queries from the diabetes-related online community, dated between 1 December 2019 and 3 May 2022, were used in the study. The collected data were preprocessed for bidirectional encoder representation from transformer topic modeling analysis. Topics were extracted using the class-based term frequency-inverse document frequency for nouns and verbs. From the extracted verbs, words with common definitions were subject to substitution and unification processes, which enabled the identification of multifrequent verb categories by noun topics. The following nine noun topics were extracted, in this order: dietary management, drug management, gestational and childhood diabetes, management of diabetic complications, use and cost of medical treatment, blood glucose management, exercise treatment, COVID-19 vaccine and complications, and diabetes in older adults. The top three verb categories by noun topics were permission, method, and possibility. This study provided baseline data that can be used by clinical nurses to deliver diabetes-related education and management based on information sought by patients.
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Registered trials
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.