Evidence map›Paper›PMID 30961624›Full record

ArticleBMC medical informatics and decision making2019

Discovering thematic change and evolution of utilizing social media for healthcare research.

Xieling Chen, Yonghui Lun, Jun Yan, Tianyong Hao, Heng Weng

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  9. Predicting Infectious Diseases: A Bibliometric Review on Africa.International journal of environmental research and public health · 2022
    Review
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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

5 authors.

Xieling ChenCollege of Economics, Jinan University, Guangzhou, China.
Yonghui LunGuangzhou Huagong Information Software Co., Ltd., Guangzhou, China.
Jun YanAI Lab, Yidu Cloud (Beijing) Technology Co., Ltd., Beijing, China.
Tianyong HaoSchool of Computer Science, South China Normal University, Guangzhou, China. haoty@m.scnu.edu.cn.
Heng WengThe Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China. ww128@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSocial media plays a more and more important role in the research of health and healthcare due to the fast development of internet communication and information exchange. This paper conducts a bibliometric analysis to discover the thematic change and evolution of utilizing social media for healthcare research field.

methodsWith the basis of 4361 publications from both Web of Science and PubMed during the year 2008-2017, the analysis utilizes methods including topic modelling and science mapping analysis.

resultsUtilizing social media for healthcare research has attracted increasing attention from scientific communities. Journal of Medical Internet Research is the most prolific journal with the USA dominating in the research. Overly, major research themes such as YouTube analysis and Sex event are revealed. Themes in each time period and how they evolve across time span are also detected.

conclusionsThis systematic mapping of the research themes and research areas helps identify research interests and how they evolve across time, as well as providing insight into future research direction.

Indexed as

Health Services ResearchSocial MediaBibliometricsHumansPubMedHealthcare researchScience mappingSocial mediaThematic detectionThematic evolutionTopic modelling

Identifiers

PMID30961624
PMCPMC6454597

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.