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基于推特的众包:哪些措施有助于更快终结新冠疫情?

Twitter-based crowdsourcing: What kind of measures can help to end the COVID-19 pandemic faster?

作者信息

Mondal Himel, Parvanov Emil D, Singla Rajeev K, Rayan Rehab A, Nawaz Faisal A, Ritschl Valentin, Eibensteiner Fabian, Siva Sai Chandragiri, Cenanovic Merisa, Devkota Hari Prasad, Hribersek Mojca, De Ronita, Klager Elisabeth, Kletecka-Pulker Maria, Völkl-Kernstock Sabine, Khalid Garba M, Lordan Ronan, Găman Mihnea-Alexandru, Shen Bairong, Stamm Tanja, Willschke Harald, Atanasov Atanas G

机构信息

Saheed Laxman Nayak Medical College and Hospital, Koraput, Odisha, India.

Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.

出版信息

Front Med (Lausanne). 2022 Sep 16;9:961360. doi: 10.3389/fmed.2022.961360. eCollection 2022.

DOI:10.3389/fmed.2022.961360
PMID:36186802
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9523003/
Abstract

BACKGROUND

Crowdsourcing is a low-cost, adaptable, and innovative method to collect ideas from numerous contributors with diverse backgrounds. Crowdsourcing from social media like Twitter can be used for generating ideas in a noticeably brief time based on contributions from globally distributed users. The world has been challenged by the COVID-19 pandemic in the last several years. Measures to combat the pandemic continue to evolve worldwide, and ideas and opinions on optimal counteraction strategies are of high interest.

OBJECTIVE

This study aimed to validate the use of Twitter as a crowdsourcing platform in order to gain an understanding of public opinion on what measures can help to end the COVID-19 pandemic faster.

METHODS

This cross-sectional study was conducted during the period from December 22, 2021, to February 4, 2022. Tweets were posted by accounts operated by the authors, asking "How to faster end the COVID-19 pandemic?" and encouraging the viewers to comment on measures that they perceive would be effective to achieve this goal. The ideas from the users' comments were collected and categorized into two major themes - personal and institutional measures. In the final stage of the campaign, a Twitter poll was conducted to get additional comments and to estimate which of the two groups of measures were perceived to be important amongst Twitter users.

RESULTS

The crowdsourcing campaign generated seventeen suggested measures categorized into two major themes (personal and institutional) that received a total of 1,727 endorsements (supporting comments, retweets, and likes). The poll received a total of 325 votes with 58% of votes underscoring the importance of both personal and institutional measures, 20% favoring personal measures, 11% favoring institutional measures, and 11% of the votes given just out of curiosity to see the vote results.

CONCLUSIONS

Twitter was utilized successfully for crowdsourcing ideas on strategies how to end the COVID-19 pandemic faster. The results indicate that the Twitter community highly values the significance of both personal responsibility and institutional measures to counteract the pandemic. This study validates the use of Twitter as a primary tool that could be used for crowdsourcing ideas with healthcare significance.

摘要

背景

众包是一种低成本、适应性强且具有创新性的方法,可从众多背景各异的贡献者那里收集想法。像推特这样的社交媒体众包可基于全球分布用户的贡献,在极短时间内产生想法。在过去几年里,世界受到了新冠疫情的挑战。全球范围内对抗疫情的措施不断演变,关于最佳应对策略的想法和观点备受关注。

目的

本研究旨在验证推特作为众包平台的用途,以便了解公众对于哪些措施有助于更快终结新冠疫情的看法。

方法

这项横断面研究于2021年12月22日至2022年2月4日期间进行。作者运营的账号发布推文,询问“如何更快终结新冠疫情?”,并鼓励观众评论他们认为对实现这一目标有效的措施。收集用户评论中的想法,并将其分为两个主要主题——个人措施和机构措施。在活动的最后阶段,进行了一项推特民意调查,以获取更多评论,并估计在推特用户中这两组措施中哪一组被认为是重要的。

结果

众包活动产生了17项建议措施,分为两个主要主题(个人和机构),共获得1727次认可(支持性评论、转发和点赞)。民意调查共收到325票,58%的选票强调个人和机构措施的重要性,20%支持个人措施,11%支持机构措施,11%的选票只是出于好奇想看看投票结果。

结论

推特成功用于众包关于如何更快终结新冠疫情的策略想法。结果表明,推特社区高度重视个人责任和机构措施在对抗疫情方面的重要性。本研究验证了推特作为可用于众包具有医疗保健意义想法的主要工具的用途。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f30e/9523003/cef65f0c4906/fmed-09-961360-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f30e/9523003/31c49428e632/fmed-09-961360-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f30e/9523003/cef65f0c4906/fmed-09-961360-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f30e/9523003/31c49428e632/fmed-09-961360-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f30e/9523003/cef65f0c4906/fmed-09-961360-g0002.jpg

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