Online profiling of volunteers in public health emergencies: insights from COVID-19 in China DOI Creative Commons

Hongzhou Shen,

Qirui Chen,

Changcheng Li

et al.

BMC Public Health, Journal Year: 2025, Volume and Issue: 25(1)

Published: Jan. 20, 2025

During public health emergencies, the diverse backgrounds of volunteers pose numerous management challenges. This study aims to develop an online profiling model using social media data achieve a more comprehensive and objective understanding them. In proposed model, designed five tags: basic information, sentiment, topic features, interest preferences, engagement. K-Modes clustering was employed implement profiling. To validate feasibility empirical conducted Weibo from 1,070 during COVID-19 pandemic in China, resulting these volunteers. Four categories could be identified: Public Affairs Pioneers (32.4%), Diary Record Lurkers (32.8%), Social Topic Sharers (20.9%), Fashion Entertainment Influencers (13.9%). Overall, were predominantly female, generally interested entertainment, relatively satisfied with their volunteer work, possessed sense responsibility. The four exhibited distinct characteristics terms interests, behavior, influence. objectively captures emergencies. identified through results provide multidimensional For different categories, official agencies can tailor recruitment, management, training strategies better suit specific needs strengths volunteers, thereby enhancing effectiveness efficiency engagement ensuring are well-prepared supported roles.

Language: Английский

Online profiling of volunteers in public health emergencies: insights from COVID-19 in China DOI Creative Commons

Hongzhou Shen,

Qirui Chen,

Changcheng Li

et al.

BMC Public Health, Journal Year: 2025, Volume and Issue: 25(1)

Published: Jan. 20, 2025

During public health emergencies, the diverse backgrounds of volunteers pose numerous management challenges. This study aims to develop an online profiling model using social media data achieve a more comprehensive and objective understanding them. In proposed model, designed five tags: basic information, sentiment, topic features, interest preferences, engagement. K-Modes clustering was employed implement profiling. To validate feasibility empirical conducted Weibo from 1,070 during COVID-19 pandemic in China, resulting these volunteers. Four categories could be identified: Public Affairs Pioneers (32.4%), Diary Record Lurkers (32.8%), Social Topic Sharers (20.9%), Fashion Entertainment Influencers (13.9%). Overall, were predominantly female, generally interested entertainment, relatively satisfied with their volunteer work, possessed sense responsibility. The four exhibited distinct characteristics terms interests, behavior, influence. objectively captures emergencies. identified through results provide multidimensional For different categories, official agencies can tailor recruitment, management, training strategies better suit specific needs strengths volunteers, thereby enhancing effectiveness efficiency engagement ensuring are well-prepared supported roles.

Language: Английский

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