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. 2020 Nov 3;15(11):e0241465.
doi: 10.1371/journal.pone.0241465. eCollection 2020.

What triggers online help-seeking retransmission during the COVID-19 period? Empirical evidence from Chinese social media

Affiliations

What triggers online help-seeking retransmission during the COVID-19 period? Empirical evidence from Chinese social media

Chen Luo et al. PLoS One. .

Abstract

The past nine months witnessed COVID-19's fast-spreading at the global level. Limited by medical resources shortage and uneven facilities distribution, online help-seeking becomes an essential approach to cope with public health emergencies for many ordinaries. This study explores the driving forces behind the retransmission of online help-seeking posts. We built an analytical framework that emphasized content characteristics, including information completeness, proximity, support seeking type, disease severity, and emotion of help-seeking messages. A quantitative content analysis was conducted with a probability sample consisting of 727 posts. The results illustrate the importance of individual information completeness, high proximity, instrumental support seeking. This study also demonstrates slight inconformity with the severity principle but stresses the power of anger in help-seeking messages dissemination. As one of the first online help-seeking diffusion analyses in the COVID-19 period, our research provides a reference for constructing compelling and effective help-seeking posts during a particular period. It also reveals further possibilities for harnessing social media's power to promote reciprocal and cooperative actions as a response to this deepening global concern.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Model comparison based on residuals.
The line with hollow circles represents the zero-inflated negative binomial regression model, and the line with solid triangles represents the negative binomial regression model. Small residuals are indicative of good-fitting model, the model with the line closest to zero should be considered for our research data.

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