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      A multidimensional comparative study of help-seeking messages on Weibo under different stages of COVID-19 pandemic in China

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          Abstract

          Objective

          During the COVID-19 pandemic, people posted help-seeking messages on Weibo, a mainstream social media in China, to solve practical problems. As viruses, policies, and perceptions have all changed, help-seeking behavior on Weibo has been shown to evolve in this paper.

          Methods

          We compare and analyze the help-seeking messages from three dimensions: content categories, time distribution, and retweeting influencing factors. First, we crawled the help-seeking messages from Weibo, and successively used CNN and xlm-roberta-large models for text classification to analyze the changes of help-seeking messages in different stages from the content categories dimension. Subsequently, we studied the time distribution of help-seeking messages and calculated the time lag using TLCC algorithm. Finally, we analyze the changes of the retweeting influencing factors of help-seeking messages in different stages by negative binomial regression.

          Results

          (1) Help-seekers in different periods have different emphasis on content. (2) There is a significant correlation between new daily help-seeking messages and new confirmed cases in the middle stage (1/1/2022–5/20/2022), with a 16-day time lag, but there is no correlation in the latter stage (12/10/2022–2/25/2023). (3) In all the periods, pictures or videos, and the length of the text have a significant positive effect on the number of retweets of help-seeking messages, but other factors do not have exactly the same effect on the retweeting volume.

          Conclusion

          This paper demonstrates the evolution of help-seeking messages during different stages of the COVID-19 pandemic in three dimensions: content categories, time distribution, and retweeting influencing factors, which are worthy of reference for decision-makers and help-seekers, as well as provide thinking for subsequent studies.

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          Most cited references54

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              An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China

              Huaiyu Tian, Yonghong Liu, Yidan Li … (2020)
              Responding to an outbreak of a novel coronavirus (agent of COVID-19) in December 2019, China banned travel to and from Wuhan city on 23 January and implemented a national emergency response. We investigated the spread and control of COVID-19 using a unique data set including case reports, human movement and public health interventions. The Wuhan shutdown was associated with the delayed arrival of COVID-19 in other cities by 2.91 days (95%CI: 2.54-3.29). Cities that implemented control measures pre-emptively reported fewer cases, on average, in the first week of their outbreaks (13.0; 7.1-18.8) compared with cities that started control later (20.6; 14.5-26.8). Suspending intra-city public transport, closing entertainment venues and banning public gatherings were associated with reductions in case incidence. The national emergency response appears to have delayed the growth and limited the size of the COVID-19 epidemic in China, averting hundreds of thousands of cases by 19 February (day 50).
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                Author and article information

                Contributors
                Jianhong Jiang: URI : https://loop.frontiersin.org/people/2543849/overviewRole: Role: Role: Role: Role:
                Chenyan Yao: URI : https://loop.frontiersin.org/people/2541119/overviewRole: Role: Role: Role: Role:
                Xinyi Song: Role: Role:
                Journal
                Journal ID (nlm-ta): Front Public Health
                Journal ID (iso-abbrev): Front Public Health
                Journal ID (publisher-id): Front. Public Health
                Title: Frontiers in Public Health
                Publisher: Frontiers Media S.A.
                ISSN (Electronic): 2296-2565
                Publication date (Electronic): 14 February 2024
                Publication date Collection: 2024
                Volume: 12
                Electronic Location Identifier: 1320146
                Affiliations
                School of Business, Guilin University of Electronic Technology , Guilin, Guangxi, China
                Author notes

                Edited by: Mohammadreza Shalbafan, Iran University of Medical Sciences, Iran

                Reviewed by: Dongyan Nan, Sungkyunkwan University, Republic of Korea; Yiwei Xu, University of Washington, United States

                *Correspondence: Chenyan Yao, dyoccc@ 123456163.com
                Article
                DOI: 10.3389/fpubh.2024.1320146
                PMC ID: 10899892
                PubMed ID: 38420033
                SO-VID: 4437e087-8624-49fd-8a0b-393515a838de
                Copyright © Copyright © 2024 Jiang, Yao and Song.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                Date received : 11 October 2023
                Date accepted : 31 January 2024
                Page count
                Figures: 7, Tables: 7, Equations: 2, References: 72, Pages: 17, Words: 12828
                Funding
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (grant number 71940008).
                Categories
                Subject: Public Health
                Subject: Original Research
                Custom metadata
                section-at-acceptance Digital Public Health

                Keywords: covid-19,help-seeking behavior,social media,data mining,neural networks,regression analysis
                Data availability:
                Keywords: covid-19, help-seeking behavior, social media, data mining, neural networks, regression analysis

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