Evidence map›Paper›PMID 37358900›Full record

ArticleJournal of medical Internet research2023

Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review.

Jiaqi Fu, Chaixiu Li, Chunlan Zhou, Wenji Li, Jie Lai, Shisi Deng, Yujie Zhang, Zihan Guo, Yanni Wu

Abstract readScoping Review
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

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.

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

20 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Observational
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Exploring How Rheumatic Fever Is Portrayed on TikTok: A Descriptive Content Analysis.International journal of environmental research and public health · 2025
    Article
  13. Article
  14. Article
  15. Article
  16. ChatGPT for Automated Qualitative Research: Content Analysis.Journal of medical Internet research · 2024
    Article
  17. Article
  18. Review
  19. Article
  20. Article
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

9 authors.

Jiaqi Fu *Nanfang Hospital, Southern Medical University, Guangzhou, China.ORCID 0000-0001-5353-6835
Chaixiu Li *Nanfang Hospital, Southern Medical University, Guangzhou, China.ORCID 0000-0003-2303-0643
Chunlan ZhouNanfang Hospital, Southern Medical University, Guangzhou, China.ORCID 0000-0003-1998-5594
Wenji LiNanfang Hospital, Southern Medical University, Guangzhou, China.ORCID 0000-0002-6428-3623
Jie LaiNanfang Hospital, Southern Medical University, Guangzhou, China.ORCID 0000-0003-4919-8196
Shisi DengNanfang Hospital, Southern Medical University, Guangzhou, China.ORCID 0009-0003-1454-7592
Yujie ZhangNanfang Hospital, Southern Medical University, Guangzhou, China.ORCID 0009-0009-9305-2718
Zihan GuoNanfang Hospital, Southern Medical University, Guangzhou, China.ORCID 0009-0005-3375-1075
Yanni WuNanfang Hospital, Southern Medical University, Guangzhou, China.ORCID 0000-0003-1938-2785

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGiven the rapid development of social media, effective extraction and analysis of the contents of social media for health care have attracted widespread attention from health care providers. As far as we know, most of the reviews focus on the application of social media, and there is a lack of reviews that integrate the methods for analyzing social media information for health care.

objectiveThis scoping review aims to answer the following 4 questions: (1) What types of research have been used to investigate social media for health care, (2) what methods have been used to analyze the existing health information on social media, (3) what indicators should be applied to collect and evaluate the characteristics of methods for analyzing the contents of social media for health care, and (4) what are the current problems and development directions of methods used to analyze the contents of social media for health care?

methodsA scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted. We searched PubMed, the Web of Science, EMBASE, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library for the period from 2010 to May 2023 for primary studies focusing on social media and health care. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted.

resultsOf 16,161 identified citations, 134 (0.8%) studies were included in this review. These included 67 (50.0%) qualitative designs, 43 (32.1%) quantitative designs, and 24 (17.9%) mixed methods designs. The applied research methods were classified based on the following aspects: (1) manual analysis methods (content analysis methodology, grounded theory, ethnography, classification analysis, thematic analysis, and scoring tables) and computer-aided analysis methods (latent Dirichlet allocation, support vector machine, probabilistic clustering, image analysis, topic modeling, sentiment analysis, and other natural language processing technologies), (2) categories of research contents, and (3) health care areas (health practice, health services, and health education).

conclusionsBased on an extensive literature review, we investigated the methods for analyzing the contents of social media for health care to determine the main applications, differences, trends, and existing problems. We also discussed the implications for the future. Traditional content analysis is still the mainstream method for analyzing social media content, and future research may be combined with big data research. With the progress of computers, mobile phones, smartwatches, and other smart devices, social media information sources will become more diversified. Future research can combine new sources, such as pictures, videos, and physiological signals, with online social networking to adapt to the development trend of the internet. More medical information talents need to be trained in the future to better solve the problem of network information analysis. Overall, this scoping review can be useful for a large audience that includes researchers entering the field.

Indexed as

Cell PhoneSocial MediaDelivery of Health CareHealth EducationHealth ServicesHumansbig data miningcontent analysishealth careinternet informationmethodologyonline informationreview methodscopingsocial media

Identifiers

PMID37358900
PMCPMC10337469

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.