Evidence map›Paper›PMID 39833777›Full record

ArticleBMC public health2025

Online profiling of volunteers in public health emergencies: insights from COVID-19 in China.

Hongzhou Shen, Qirui Chen, Changcheng Li

Abstract read
In one paragraph

Article in BMC public health, 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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0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

The trial behind it

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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

3 authors.

Hongzhou ShenSchool of Management, Nanjing University of Posts and Telecommunications, No.66 Xin Mofan Road, Nanjing, Jiangsu, 210003, China. hzshen@njupt.edu.cn.
Qirui ChenSchool of Management, Nanjing University of Posts and Telecommunications, No.66 Xin Mofan Road, Nanjing, Jiangsu, 210003, China.
Changcheng LiSchool of Management, Nanjing University of Posts and Telecommunications, No.66 Xin Mofan Road, Nanjing, Jiangsu, 210003, China.

Funding

National Natural Science Foundation of China 71974102
6 · The paper itself

Abstract

backgroundDuring public health emergencies, the diverse backgrounds of volunteers pose numerous management challenges. This study aims to develop an online profiling model of volunteers using social media data to achieve a more comprehensive and objective understanding of them.

methodsIn the proposed model, the study designed five profiling tags: basic information, sentiment, topic features, interest preferences, and online social engagement. K-Modes clustering was employed to implement the profiling. To validate the feasibility of the model, an empirical study was conducted using Weibo data from 1,070 volunteers during the COVID-19 pandemic in China, resulting in the online profiling of these volunteers.

resultsFour categories of volunteers could be identified: Public Affairs Pioneers (32.4%), Diary Record Lurkers (32.8%), Social Topic Sharers (20.9%), and Fashion and Entertainment Influencers (13.9%). Overall, volunteers were predominantly female, generally interested in entertainment, relatively satisfied with their volunteer work, and possessed a sense of social responsibility. The four categories of volunteers exhibited distinct characteristics in terms of interests, online social behavior, and influence.

conclusionsThe proposed online profiling model objectively captures the characteristics of volunteers during public health emergencies. The four volunteer categories identified through the empirical results provide a multidimensional and comprehensive understanding of volunteers. For different volunteer categories, official agencies can tailor their recruitment, management, and training strategies to better suit the specific needs and strengths of the volunteers, thereby enhancing the effectiveness and efficiency of volunteer engagement and ensuring volunteers are well-prepared and supported in their roles.

Indexed as

COVID-19EmergenciesPublic HealthSocial MediaVolunteersAdultChinaFemaleHumansMaleMiddle AgedPandemicsSARS-CoV-2COVID-19Online profilingPublic health emergenciesSocial mediaVolunteersWeibo

Identifiers

PMID39833777
PMCPMC11744825

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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.