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

COVID-19 Twitter crowdsourcing pandemic public opinion social media

Journal

Frontiers in medicine
ISSN: 2296-858X
Titre abrégé: Front Med (Lausanne)
Pays: Switzerland
ID NLM: 101648047

Informations de publication

Date de publication:
2022
Historique:
received: 04 06 2022
accepted: 24 08 2022
entrez: 3 10 2022
pubmed: 4 10 2022
medline: 4 10 2022
Statut: epublish

Résumé

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. 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. 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. 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. 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.

Sections du résumé

Background UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
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.

Identifiants

pubmed: 36186802
doi: 10.3389/fmed.2022.961360
pmc: PMC9523003
doi:

Types de publication

Journal Article

Langues

eng

Pagination

961360

Informations de copyright

Copyright © 2022 Mondal, Parvanov, Singla, Rayan, Nawaz, Ritschl, Eibensteiner, Siva Sai, Cenanovic, Devkota, Hribersek, De, Klager, Kletecka-Pulker, Völkl-Kernstock, Khalid, Lordan, Găman, Shen, Stamm, Willschke and Atanasov.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Himel Mondal (H)

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

Emil D Parvanov (ED)

Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.
Department of Translational Stem Cell Biology, Research Institute of the Medical University of Varna, Varna, Bulgaria.

Rajeev K Singla (RK)

Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, India.

Rehab A Rayan (RA)

Department of Epidemiology, High Institute of Public Health, Alexandria University, Alexandria, Egypt.

Faisal A Nawaz (FA)

College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates.

Valentin Ritschl (V)

Section for Outcomes Research, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
Ludwig Boltzmann Institute for Arthritis and Rehabilitation, Vienna, Austria.

Fabian Eibensteiner (F)

Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.
Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria.

Chandragiri Siva Sai (C)

Amity Institute of Pharmacy, Amity University, Lucknow Campus, Lucknow, Uttar Pradesh, India.

Merisa Cenanovic (M)

Independent Researcher, Sarajevo, Bosnia and Herzegovina.

Hari Prasad Devkota (HP)

Graduate School of Pharmaceutical Sciences, Kumamoto University, Kumamoto, Japan.
Headquarters for Admissions and Education, Kumamoto University, Kumamoto, Japan.

Mojca Hribersek (M)

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

Ronita De (R)

ICMR-National Institute of Cholera and Enteric Diseases, Kolkata, West Bengal, India.

Elisabeth Klager (E)

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

Maria Kletecka-Pulker (M)

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

Sabine Völkl-Kernstock (S)

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

Garba M Khalid (GM)

Pharmaceutical Engineering Group, School of Pharmacy, Queen's University, Belfast, United Kingdom.

Ronan Lordan (R)

Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.

Mihnea-Alexandru Găman (MA)

Faculty of Medicine, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania.
Department of Hematology, Center of Hematology and Bone Marrow Transplantation, Fundeni Clinical Institute, Bucharest, Romania.

Bairong Shen (B)

Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.

Tanja Stamm (T)

Section for Outcomes Research, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
Ludwig Boltzmann Institute for Arthritis and Rehabilitation, Vienna, Austria.

Harald Willschke (H)

Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.
Department of Anaesthesia, Intensive Care Medicine and Pain Medicine, Medical University Vienna, Vienna, Austria.

Atanas G Atanasov (AG)

Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.
Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Jastrzẹbiec, Poland.

Classifications MeSH