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. 2022 Nov:162:113752.
doi: 10.1016/j.dss.2022.113752. Epub 2022 Feb 12.

Impact of information timeliness and richness on public engagement on social media during COVID-19 pandemic: An empirical investigation based on NLP and machine learning

Kai Li  1 , Cheng Zhou  1 , Xin Robert Luo  2 , Jose Benitez  3 , Qinyu Liao  4
Affiliations

Impact of information timeliness and richness on public engagement on social media during COVID-19 pandemic: An empirical investigation based on NLP and machine learning

Kai Li et al. Decis Support Syst. 2022 Nov.

Abstract

This paper investigates how information timeliness and richness affect public engagement using text data from China's largest social media platform during times of the COVID-19 pandemic. We utilize a similarity calculation method based on natural language processing (NLP) and text mining to evaluate three dimensions of information timeliness: retrospectiveness, immediateness, and prospectiveness. Public engagement is divided into breadth and depth. The empirical results show that information retrospectiveness is negatively associated with public engagement breadth but positively with depth. Both information immediateness and prospectiveness improved the breadth and depth of public engagement. Interestingly, information richness has a positive moderating effect on the relationships between information retrospectiveness, prospectiveness, and public engagement breadth but no significant effects on immediateness; meanwhile, it has a negative moderating effect on the relationship between retrospectiveness and depth but a positive effect on immediateness, prospectiveness. In the extension analysis, we constructed a supervised NLP model to identify and classify health emergency-related information (epidemic prevention and help-seeking) automatically. We find that public engagement differs in the two emergency-related information categories. The findings can promote a more responsive public health strategy that magnifies the transfer speed for critical information and mitigates the negative impacts of information uncertainty or false information.

Keywords: Health emergencies; Information richness; Information timeliness; Natural language processing (NLP) for societal benefit; Public engagement; Social media.

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Figures

Fig. 1
Fig. 1
Concept of information timeliness.
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Research model.
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The process of the research procedures.
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Fig. 4
The moderating effects of information richness on information timeliness on public engagement breadth.
Fig. 5
Fig. 5
The moderating effects of information richness on the relationship between information timeliness and public engagement depth.
See this image and copyright information in PMC

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