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1
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Bahreini R, Sardareh M, Arab-Zozani M. A scoping review of COVID-19 vaccine hesitancy: refusal rate, associated factors, and strategies to reduce. Front Public Health 2024; 12:1382849. [PMID: 39473604 PMCID: PMC11518786 DOI: 10.3389/fpubh.2024.1382849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 09/17/2024] [Indexed: 11/15/2024] Open
Abstract
Objective This study aimed to investigate the evidence regarding vaccine hesitancy including refusal rate, associated factors, and potential strategies to reduce it. Methods This is a scoping review. Three main databases such as PubMed, Scopus, and Web of Science were searched from 1 January 2020 to 1 January 2023. All original studies in the English language that investigated one of our domains (vaccine hesitancy rate, factors associated with vaccine hesitancy, and the ways/interventions to overcome or decrease vaccine hesitancy) among the general population were included in this study. The data were charted using tables and figures. In addition, a content analysis was conducted using the 3C model of vaccine hesitancy (Confidence, Complacency, and Convenience) that was previously introduced by the WHO. Results Finally, 184 studies were included in this review. Of these, 165, 181, and 124 studies reported the vaccine hesitancy rate, associated factors, and interventions to reduce or overcome vaccine hesitancy, respectively. Factors affecting the hesitancy rate were categorized into 4 themes and 18 sub-themes (contextual factors, confidence barriers, complacency barriers, and convenience barriers). Conclusion Vaccine hesitancy (VH) rate and the factors affecting it are different according to different populations, contexts, and data collection tools that need to be investigated in specific populations and contexts. The need to conduct studies at the national and international levels regarding the reasons for vaccine refusal, the factors affecting it, and ways to deal with it still remains. Designing a comprehensive tool will facilitate comparisons between different populations and different locations.
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Affiliation(s)
- Rona Bahreini
- Iranian Center of Excellence in Health Management (IceHM), Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mehran Sardareh
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Morteza Arab-Zozani
- Social Determinants of Health Research Center, Birjand University of Medical Sciences, Birjand, Iran
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2
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Hirabayashi M, Shibata D, Shinohara E, Kawazoe Y. Influence of Tweets Indicating False Rumors on COVID-19 Vaccination: Case Study. JMIR Form Res 2023; 7:e45867. [PMID: 37669092 PMCID: PMC10482055 DOI: 10.2196/45867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 07/27/2023] [Accepted: 08/01/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND As of December 2022, the outbreak of COVID-19 showed no sign of abating, continuing to impact people's lives, livelihoods, economies, and more. Vaccination is an effective way to achieve mass immunity. However, in places such as Japan, where vaccination is voluntary, there are people who choose not to receive the vaccine, even if an effective vaccine is offered. To promote vaccination, it is necessary to clarify what kind of information on social media can influence attitudes toward vaccines. OBJECTIVE False rumors and counterrumors are often posted and spread in large numbers on social media, especially during emergencies. In this paper, we regard tweets that contain questions or point out errors in information as counterrumors. We analyze counterrumors tweets related to the COVID-19 vaccine on Twitter. We aimed to answer the following questions: (1) what kinds of COVID-19 vaccine-related counterrumors were posted on Twitter, and (2) are the posted counterrumors related to social conditions such as vaccination status? METHODS We use the following data sets: (1) counterrumors automatically collected by the "rumor cloud" (18,593 tweets); and (2) the number of COVID-19 vaccine inoculators from September 27, 2021, to August 15, 2022, published on the Prime Minister's Office's website. First, we classified the contents contained in counterrumors. Second, we counted the number of COVID-19 vaccine-related counterrumors from data set 1. Then, we examined the cross-correlation coefficients between the numbers of data sets 1 and 2. Through this verification, we examined the correlation coefficients for the following three periods: (1) the same period of data; (2) the case where the occurrence of the suggestion of counterrumors precedes the vaccination (negative time lag); and (3) the case where the vaccination precedes the occurrence of counterrumors (positive time lag). The data period used for the validation was from October 4, 2021, to April 18, 2022. RESULTS Our classification results showed that most counterrumors about the COVID-19 vaccine were negative. Moreover, the correlation coefficients between the number of counterrumors and vaccine inoculators showed significant and strong positive correlations. The correlation coefficient was over 0.7 at -8, -7, and -1 weeks of lag. Results suggest that the number of vaccine inoculators tended to increase with an increase in the number of counterrumors. Significant correlation coefficients of 0.5 to 0.6 were observed for lags of 1 week or more and 2 weeks or more. This implies that an increase in vaccine inoculators increases the number of counterrumors. These results suggest that the increase in the number of counterrumors may have been a factor in inducing vaccination behavior. CONCLUSIONS Using quantitative data, we were able to reveal how counterrumors influence the vaccination status of the COVID-19 vaccine. We think that our findings would be a foundation for considering countermeasures of vaccination.
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Affiliation(s)
- Mai Hirabayashi
- Artificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Daisaku Shibata
- Artificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Emiko Shinohara
- Artificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yoshimasa Kawazoe
- Artificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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3
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Fasce A, Schmid P, Holford DL, Bates L, Gurevych I, Lewandowsky S. A taxonomy of anti-vaccination arguments from a systematic literature review and text modelling. Nat Hum Behav 2023; 7:1462-1480. [PMID: 37460761 DOI: 10.1038/s41562-023-01644-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 05/25/2023] [Indexed: 09/23/2023]
Abstract
The proliferation of anti-vaccination arguments is a threat to the success of many immunization programmes. Effective rebuttal of contrarian arguments requires an approach that goes beyond addressing flaws in the arguments, by also considering the attitude roots-that is, the underlying psychological attributes driving a person's belief-of opposition to vaccines. Here, through a pre-registered systematic literature review of 152 scientific articles and thematic analysis of anti-vaccination arguments, we developed a hierarchical taxonomy that relates common arguments and themes to 11 attitude roots that explain why an individual might express opposition to vaccination. We further validated our taxonomy on coronavirus disease 2019 anti-vaccination misinformation, through a combination of human coding and machine learning using natural language processing algorithms. Overall, the taxonomy serves as a theoretical framework to link expressed opposition of vaccines to their underlying psychological processes. This enables future work to develop targeted rebuttals and other interventions that address the underlying motives of anti-vaccination arguments.
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Affiliation(s)
- Angelo Fasce
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal.
| | - Philipp Schmid
- Institute for Planetary Health Behaviour, University of Erfurt, Erfurt, Germany
- Department of Implementation Research, Bernhard-Nocht-Institute for Tropical Medicine, Hamburg, Germany
| | - Dawn L Holford
- School of Psychological Science, University of Bristol, Bristol, UK
- Department of Psychology, University of Essex, Colchester, UK
| | - Luke Bates
- Ubiquitous Knowledge Processing Lab/Department of Computer Science and Hessian Center for AI (hessian.AI), Technical University of Darmstadt, Darmstadt, Germany
| | - Iryna Gurevych
- Ubiquitous Knowledge Processing Lab/Department of Computer Science and Hessian Center for AI (hessian.AI), Technical University of Darmstadt, Darmstadt, Germany
| | - Stephan Lewandowsky
- School of Psychological Science, University of Bristol, Bristol, UK
- School of Psychological Science, University of Western Australia, Perth, Western Australia, Australia
- Department of Psychology, University of Potsdam, Potsdam, Germany
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Korolchuk O, Vasiuk N, Klymkova I, Shvets D, Piddubnyi O. COVID-19 Vaccination under Conditions of War in Ukraine. Asian Bioeth Rev 2023. [PMCID: PMC10071466 DOI: 10.1007/s41649-023-00248-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023] Open
Abstract
The COVID-19 pandemic, which spread around the world in 2020, changed the lives of millions of people and affected the life and functioning of all countries and people without exception. With the emergence of the opportunity to be vaccinated against COVID-19, the problem of making a decision about vaccination also appeared. But it has become increasingly clear that the coronavirus is moving into the group of annual viral epidemic diseases that occur every year in different countries during the seasonal wave of acute respiratory viral infections. The ongoing COVID-19 pandemic against the background of the adoption of serious quarantine measures indicates the need for large-scale vaccination of the population as the most effective way to protect against COVID-19. In this article, we pay special attention to vaccination, as the main factor in ensuring health, reducing the morbidity and severity of the course of the COVID-19 disease, and an important task of the state and modern public administration.
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Affiliation(s)
- Olena Korolchuk
- grid.77512.360000 0004 0490 8008Department of Health Sciences, Faculty of Health and Physical Education, Uzhhorod National University, Uzhhorod, Ukraine
| | - Nataliia Vasiuk
- grid.445707.50000 0001 2180 4188Department of National Economy and Public Administration, Faculty of Economics and Management, Kyiv National Economic University Named After Vadym Hetman, Kiev, Ukraine
| | - Iryna Klymkova
- grid.445736.70000 0001 2180 329XDepartment of Philosophy and Social Diciplines, Interregional Academy of Personnel Management, Kiev, Ukraine
| | - Dmytro Shvets
- Odesa State University of Internal Affairs, Odesa, Ukraine
| | - Oleksii Piddubnyi
- grid.37677.320000 0004 0587 1016Department of Civil and Economic Law, National University of Life and Environmental Sciences of Ukraine, Kiev, Ukraine
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5
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Sun Y, Li X, Guo D. COVID-19 Vaccine Hesitancy in China: An Analysis of Reasons through Mixed Methods. Vaccines (Basel) 2023; 11:vaccines11030712. [PMID: 36992296 DOI: 10.3390/vaccines11030712] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/18/2023] [Accepted: 03/20/2023] [Indexed: 03/31/2023] Open
Abstract
This study aims to investigate the causes of COVID-19 vaccine hesitancy among the Chinese population. The LDA model and content analysis were used to analyze the content of COVID-19 vaccine hesitancy expressed by the Chinese on Weibo from 2020 to 2022, the leading causes of vaccine hesitancy, and the changes in the reasons for vaccine hesitancy over time. The study found that when the Chinese expressed vaccine hesitancy, it usually involved themes such as information access (18.59%), vaccination services (13.91%), and physical illness (13.24%), and topics such as vaccination process (6.83%), allergic diseases (6.59%), and international news (6.43%). Constraints (35.48%), confidence (17.94%), and calculation (15.99%) are the leading causes of vaccine hesitancy on Weibo. These findings provide a comprehensive picture of how the Chinese express vaccine hesitancy in social media and the reasons and changes for vaccine hesitancy, which can help inspire public health experts, health organizations, or governments in various countries to improve the phenomenon of vaccine hesitancy.
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Affiliation(s)
- Yao Sun
- School of Journalism and Communication, Nanjing Normal University, Nanjing 210097, China
| | - Xi Li
- School of Journalism and Communication, Nanjing Normal University, Nanjing 210097, China
| | - Difan Guo
- School of Journalism and Communication, Beijing Normal University, Beijing 100091, China
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6
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Zang S, Zhang X, Xing Y, Chen J, Lin L, Hou Z. Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review. J Med Internet Res 2023; 25:e40057. [PMID: 36649235 PMCID: PMC9924059 DOI: 10.2196/40057] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 12/18/2022] [Accepted: 01/13/2023] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Social media and digital technologies have played essential roles in disseminating information and promoting vaccination during the COVID-19 pandemic. There is a need to summarize the applications and analytical techniques of social media and digital technologies in monitoring vaccine attitudes and administering COVID-19 vaccines. OBJECTIVE We aimed to synthesize the global evidence on the applications of social media and digital technologies in COVID-19 vaccination and to explore their avenues to promote COVID-19 vaccination. METHODS We searched 6 databases (PubMed, Scopus, Web of Science, Embase, EBSCO, and IEEE Xplore) for English-language articles from December 2019 to August 2022. The search terms covered keywords relating to social media, digital technology, and COVID-19 vaccines. Articles were included if they provided original descriptions of applications of social media or digital health technologies/solutions in COVID-19 vaccination. Conference abstracts, editorials, letters, commentaries, correspondence articles, study protocols, and reviews were excluded. A modified version of the Appraisal Tool for Cross-Sectional Studies (AXIS tool) was used to evaluate the quality of social media-related studies. The review was undertaken with the guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. RESULTS A total of 178 articles were included in our review, including 114 social media articles and 64 digital technology articles. Social media has been applied for sentiment/emotion analysis, topic analysis, behavioral analysis, dissemination and engagement analysis, and information quality analysis around COVID-19 vaccination. Of these, sentiment analysis and topic analysis were the most common, with social media data being primarily analyzed by lexicon-based and machine learning techniques. The accuracy and reliability of information on social media can seriously affect public attitudes toward COVID-19 vaccines, and misinformation often leads to vaccine hesitancy. Digital technologies have been applied to determine the COVID-19 vaccination strategy, predict the vaccination process, optimize vaccine distribution and delivery, provide safe and transparent vaccination certificates, and perform postvaccination surveillance. The applied digital technologies included algorithms, blockchain, mobile health, the Internet of Things, and other technologies, although with some barriers to their popularization. CONCLUSIONS The applications of social media and digital technologies in addressing COVID-19 vaccination-related issues represent an irreversible trend. Attention should be paid to the ethical issues and health inequities arising from the digital divide while applying and promoting these technologies.
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Affiliation(s)
- Shujie Zang
- School of Public Health, Fudan University, Shanghai, China
- Global Health Institute, Fudan University, Shanghai, China
| | - Xu Zhang
- School of Public Health, Fudan University, Shanghai, China
- Global Health Institute, Fudan University, Shanghai, China
| | - Yuting Xing
- School of Public Health, Fudan University, Shanghai, China
- Global Health Institute, Fudan University, Shanghai, China
| | - Jiaxian Chen
- School of Public Health, Fudan University, Shanghai, China
| | - Leesa Lin
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, SAR, China
| | - Zhiyuan Hou
- School of Public Health, Fudan University, Shanghai, China
- Global Health Institute, Fudan University, Shanghai, China
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Ginossar T, Cruickshank IJ, Zheleva E, Sulskis J, Berger-Wolf T. Cross-platform spread: vaccine-related content, sources, and conspiracy theories in YouTube videos shared in early Twitter COVID-19 conversations. Hum Vaccin Immunother 2022; 18:1-13. [PMID: 35061560 PMCID: PMC8920146 DOI: 10.1080/21645515.2021.2003647] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/14/2021] [Accepted: 11/03/2021] [Indexed: 12/11/2022] Open
Abstract
High uptake of vaccinations is essential in fighting infectious diseases, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes the ongoing coronavirus disease 2019 (COVID-19) pandemic. Social media play a crucial role in propagating misinformation about vaccination, including through conspiracy theories and can negatively impact trust in vaccination. Users typically engage with multiple social media platforms; however, little is known about the role and content of cross-platform use in spreading vaccination-related information. This study examined the content and dynamics of YouTube videos shared in vaccine-related tweets posted to COVID-19 conversations before the COVID-19 vaccine rollout. We screened approximately 144 million tweets posted to COVID-19 conversations and identified 930,539 unique tweets in English that discussed vaccinations posted between 1 February and 23 June 2020. We then identified links to 2,097 unique YouTube videos that were tweeted. Analysis of the video transcripts using Latent Dirichlet Allocation topic modeling and independent coders indicate the dominance of conspiracy theories. Following the World Health Organization's declaration of the COVID-19 outbreak as a public health emergency of international concern, anti-vaccination frames rapidly transitioned from claiming that vaccines cause autism to pandemic conspiracy theories, often featuring Bill Gates. Content analysis of the 20 most tweeted videos revealed that the majority (n = 15) opposed vaccination and included conspiracy theories. Their spread on Twitter was consistent with spamming and coordinated efforts. These findings show the role of cross-platform sharing of YouTube videos over Twitter as a strategy to propagate primarily anti-vaccination messages. Future policies and interventions should consider how to counteract misinformation spread via such cross-platform activities.
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Affiliation(s)
- Tamar Ginossar
- Department of Communication and Journalism, Institute for Social Research, The University of New Mexico, Albuquerque, NM, USA
| | - Iain J. Cruickshank
- Institute for Software Research, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Elena Zheleva
- Computer Science Department, University of Illinois at Chicago, Chicago, IL, USA
| | - Jason Sulskis
- Computer Science Department, University of Illinois at Chicago, Chicago, IL, USA
| | - Tanya Berger-Wolf
- Translational Data Analytics Institute, Computer Science Engineering, Electrical, Computer Engineering, and Evolution, Ecology, and Organismal Biology, Ohio State University, Columbus, OH, USA
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8
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Skafle I, Nordahl-Hansen A, Quintana DS, Wynn R, Gabarron E. Misinformation About COVID-19 Vaccines on Social Media: Rapid Review. J Med Internet Res 2022; 24:e37367. [PMID: 35816685 PMCID: PMC9359307 DOI: 10.2196/37367] [Citation(s) in RCA: 92] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/25/2022] [Accepted: 05/24/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The development of COVID-19 vaccines has been crucial in fighting the pandemic. However, misinformation about the COVID-19 pandemic and vaccines is spread on social media platforms at a rate that has made the World Health Organization coin the phrase infodemic. False claims about adverse vaccine side effects, such as vaccines being the cause of autism, were already considered a threat to global health before the outbreak of COVID-19. OBJECTIVE We aimed to synthesize the existing research on misinformation about COVID-19 vaccines spread on social media platforms and its effects. The secondary aim was to gain insight and gather knowledge about whether misinformation about autism and COVID-19 vaccines is being spread on social media platforms. METHODS We performed a literature search on September 9, 2021, and searched PubMed, PsycINFO, ERIC, EMBASE, Cochrane Library, and the Cochrane COVID-19 Study Register. We included publications in peer-reviewed journals that fulfilled the following criteria: original empirical studies, studies that assessed social media and misinformation, and studies about COVID-19 vaccines. Thematic analysis was used to identify the patterns (themes) of misinformation. Narrative qualitative synthesis was undertaken with the guidance of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 Statement and the Synthesis Without Meta-analysis reporting guideline. The risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal tool. Ratings of the certainty of evidence were based on recommendations from the Grading of Recommendations Assessment, Development and Evaluation Working Group. RESULTS The search yielded 757 records, with 45 articles selected for this review. We identified 3 main themes of misinformation: medical misinformation, vaccine development, and conspiracies. Twitter was the most studied social media platform, followed by Facebook, YouTube, and Instagram. A vast majority of studies were from industrialized Western countries. We identified 19 studies in which the effect of social media misinformation on vaccine hesitancy was measured or discussed. These studies implied that the misinformation spread on social media had a negative effect on vaccine hesitancy and uptake. Only 1 study contained misinformation about autism as a side effect of COVID-19 vaccines. CONCLUSIONS To prevent these misconceptions from taking hold, health authorities should openly address and discuss these false claims with both cultural and religious awareness in mind. Our review showed that there is a need to examine the effect of social media misinformation on vaccine hesitancy with a more robust experimental design. Furthermore, this review also demonstrated that more studies are needed from the Global South and on social media platforms other than the major platforms such as Twitter and Facebook. TRIAL REGISTRATION PROSPERO International Prospective Register of Systematic Reviews CRD42021277524; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021277524. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.31219/osf.io/tyevj.
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Affiliation(s)
- Ingjerd Skafle
- Faculty of Health, Welfare, and Organisation, Østfold University College, Halden, Norway
- Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Anders Nordahl-Hansen
- Department of Education, ICT, and Learning, Østfold University College, Halden, Norway
| | - Daniel S Quintana
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), University of Oslo, Oslo, Norway
- NevSom, Department of Rare Disorders & Disabilities, Oslo University Hospital, Oslo, Norway
| | - Rolf Wynn
- Department of Clinical Medicine, The Artic University of Norway, Tromsø, Norway
- Division of Mental Health and Substance Use, University Hospital of North Norway, Tromsø, Norway
| | - Elia Gabarron
- Department of Education, ICT, and Learning, Østfold University College, Halden, Norway
- Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway
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9
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Argyris YA, Zhang N, Bashyal B, Tan PN. Using Deep Learning to Identify Linguistic Features that Facilitate or Inhibit the Propagation of Anti- and Pro-Vaccine Content on Social Media. 2022 IEEE INTERNATIONAL CONFERENCE ON DIGITAL HEALTH (IEEE ICDH 2022) : PROCEEDINGS : HYBRID CONFERENCE, BARCELONA, SPAIN, 11-15 JULY 2022. INTERNATIONAL CONFERENCE ON DIGITAL HEALTH (2022 : BARCELONA, SPAIN; ONLINE) 2022; 2022:107-116. [PMID: 37975063 PMCID: PMC10652839 DOI: 10.1109/icdh55609.2022.00025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Anti-vaccine content is rapidly propagated via social media, fostering vaccine hesitancy, while pro-vaccine content has not replicated the opponent's successes. Despite this disparity in the dissemination of anti- and pro-vaccine posts, linguistic features that facilitate or inhibit the propagation of vaccine-related content remain less known. Moreover, most prior machine-learning algorithms classified social-media posts into binary categories (e.g., misinformation or not) and have rarely tackled a higher-order classification task based on divergent perspectives about vaccines (e.g., anti-vaccine, pro-vaccine, and neutral). Our objectives are (1) to identify sets of linguistic features that facilitate and inhibit the propagation of vaccine-related content and (2) to compare whether anti-vaccine, provaccine, and neutral tweets contain either set more frequently than the others. To achieve these goals, we collected a large set of social media posts (over 120 million tweets) between Nov. 15 and Dec. 15, 2021, coinciding with the Omicron variant surge. A two-stage framework was developed using a fine-tuned BERT classifier, demonstrating over 99 and 80 percent accuracy for binary and ternary classification. Finally, the Linguistic Inquiry Word Count text analysis tool was used to count linguistic features in each classified tweet. Our regression results show that anti-vaccine tweets are propagated (i.e., retweeted), while pro-vaccine tweets garner passive endorsements (i.e., favorited). Our results also yielded the two sets of linguistic features as facilitators and inhibitors of the propagation of vaccine-related tweets. Finally, our regression results show that anti-vaccine tweets tend to use the facilitators, while pro-vaccine counterparts employ the inhibitors. These findings and algorithms from this study will aid public health officials' efforts to counteract vaccine misinformation, thereby facilitating the delivery of preventive measures during pandemics and epidemics.
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Affiliation(s)
- Young Anna Argyris
- Dept of Media and Information, Michigan State University, East Lansing, MI
| | - Nan Zhang
- Dept of Advertising and Public Relations, Michigan State University, East Lansing, MI
| | - Bidhan Bashyal
- Dept of Computer Science and Engineering, Michigan State University, East Lansing, MI
| | - Pang-Ning Tan
- Dept of Computer Science and Engineering, Michigan State University, East Lansing, MI
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10
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Nyawa S, Tchuente D, Fosso-Wamba S. COVID-19 vaccine hesitancy: a social media analysis using deep learning. ANNALS OF OPERATIONS RESEARCH 2022:1-39. [PMID: 35729983 PMCID: PMC9202977 DOI: 10.1007/s10479-022-04792-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/17/2022] [Indexed: 06/15/2023]
Abstract
Hesitant attitudes have been a significant issue since the development of the first vaccines-the WHO sees them as one of the most critical global health threats. The increasing use of social media to spread questionable information about vaccination strongly impacts the population's decision to get vaccinated. Developing text classification methods that can identify hesitant messages on social media could be useful for health campaigns in their efforts to address negative influences from social media platforms and provide reliable information to support their strategies against hesitant-vaccination sentiments. This study aims to evaluate the performance of different machine learning models and deep learning methods in identifying vaccine-hesitant tweets that are being published during the COVID-19 pandemic. Our concluding remarks are that Long Short-Term Memory and Recurrent Neural Network models have outperformed traditional machine learning models on detecting vaccine-hesitant messages in social media, with an accuracy rate of 86% against 83%.
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Affiliation(s)
- Serge Nyawa
- Department of Information, Operations and Management Sciences, TBS Business School, 1 Place Alphonse Jourdain, 31068 Toulouse, France
| | - Dieudonné Tchuente
- Department of Information, Operations and Management Sciences, TBS Business School, 1 Place Alphonse Jourdain, 31068 Toulouse, France
| | - Samuel Fosso-Wamba
- Department of Information, Operations and Management Sciences, TBS Business School, 1 Place Alphonse Jourdain, 31068 Toulouse, France
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11
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Greyling T, Rossouw S. Positive attitudes towards COVID-19 vaccines: A cross-country analysis. PLoS One 2022; 17:e0264994. [PMID: 35271637 PMCID: PMC8912241 DOI: 10.1371/journal.pone.0264994] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 02/21/2022] [Indexed: 11/19/2022] Open
Abstract
COVID-19 severely impacted world health and, as a consequence of the measures implemented to stop the spread of the virus, also irreversibly damaged the world economy. Research shows that receiving the COVID-19 vaccine is the most successful measure to combat the virus and could also address its indirect consequences. However, vaccine hesitancy is growing worldwide and the WHO names this hesitancy as one of the top ten threats to global health. This study investigates the trend in positive attitudes towards vaccines across ten countries since a positive attitude is important. Furthermore, we investigate those variables related to having a positive attitude, as these factors could potentially increase the uptake of vaccines. We derive our text corpus from vaccine-related tweets, harvested in real-time from Twitter. Using Natural Language Processing (NLP), we derive the sentiment and emotions contained in the tweets to construct daily time-series data. We analyse a panel dataset spanning both the Northern and Southern hemispheres from 1 February 2021 to 31 July 2021. To determine the relationship between several variables and the positive sentiment (attitude) towards vaccines, we run various models, including POLS, Panel Fixed Effects and Instrumental Variables estimations. Our results show that more information about vaccines' safety and the expected side effects are needed to increase positive attitudes towards vaccines. Additionally, government procurement and the vaccine rollout should improve. Accessibility to the vaccine should be a priority, and a collective effort should be made to increase positive messaging about the vaccine, especially on social media. The results of this study contribute to the understanding of the emotional challenges associated with vaccine uptake and inform policymakers, health workers, and stakeholders who communicate to the public during infectious disease outbreaks. Additionally, the global fight against COVID-19 might be lost if the attitude towards vaccines is not improved.
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Affiliation(s)
- Talita Greyling
- School of Economics, College of Business and Economics, University of Johannesburg, Gauteng, South Africa
| | - Stephanié Rossouw
- School of Social Science & Public Policy, Faculty of Culture and Society, Auckland University of Technology, Auckland, New Zealand
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Yin H, Song X, Yang S, Li J. Sentiment analysis and topic modeling for COVID-19 vaccine discussions. WORLD WIDE WEB 2022; 25:1067-1083. [PMID: 35250362 PMCID: PMC8879179 DOI: 10.1007/s11280-022-01029-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 12/08/2021] [Accepted: 02/17/2022] [Indexed: 05/27/2023]
Abstract
The outbreak of the novel coronavirus disease (COVID-19) has been ongoing for almost two years and has had an unprecedented impact on the daily lives of people around the world. More recently, the emergence of the Delta variant of COVID-19 has once again put the world at risk. Fortunately, many countries and companies have developed vaccines for the coronavirus. As of 23 August 2021, more than 20 vaccines have been approved by the World Health Organization (WHO), bringing light to people besieged by the pandemic. The global rollout of the COVID-19 vaccine has sparked much discussion on social media platforms, such as the effectiveness and safety of the vaccine. However, there has not been much systematic analysis of public opinion on the COVID-19 vaccine. In this study, we conduct an in-depth analysis of the discussions related to the COVID-19 vaccine on Twitter. We analyze the hot topics discussed by people and the corresponding emotional polarity from the perspective of countries and vaccine brands. The results show that most people trust the effectiveness of vaccines and are willing to get vaccinated. In contrast, negative tweets tended to be associated with news reports of post-vaccination deaths, vaccine shortages, and post-injection side effects. Overall, this study uses popular Natural Language Processing (NLP) technologies to mine people's opinions on the COVID-19 vaccine on social media and objectively analyze and visualize them. Our findings can improve the readability of the confusing information on social media platforms and provide effective data support for the government and policy makers.
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Affiliation(s)
- Hui Yin
- School of IT, Deakin University, Geelong, Australia
| | - Xiangyu Song
- School of IT, Deakin University, Geelong, Australia
| | - Shuiqiao Yang
- School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
| | - Jianxin Li
- School of IT, Deakin University, Geelong, Australia
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13
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Jang H, Rempel E, Roe I, Adu PA, Carenini G, Janjua NZ. Tracking Public Attitudes toward COVID-19 Vaccination on Tweets in Canada: Using Aspect-based Sentiment Analysis. J Med Internet Res 2022; 24:e35016. [PMID: 35275835 PMCID: PMC8966890 DOI: 10.2196/35016] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 02/22/2022] [Accepted: 02/22/2022] [Indexed: 01/16/2023] Open
Abstract
Background The development and approval of COVID-19 vaccines have generated optimism for the end of the COVID-19 pandemic and a return to normalcy. However, vaccine hesitancy, often fueled by misinformation, poses a major barrier to achieving herd immunity. Objective We aim to investigate Twitter users’ attitudes toward COVID-19 vaccination in Canada after vaccine rollout. Methods We applied a weakly supervised aspect-based sentiment analysis (ABSA) technique, which involves the human-in-the-loop system, on COVID-19 vaccination–related tweets in Canada. Automatically generated aspect and opinion terms were manually corrected by public health experts to ensure the accuracy of the terms and make them more domain-specific. Then, based on these manually corrected terms, the system inferred sentiments toward the aspects. We observed sentiments toward key aspects related to COVID-19 vaccination, and investigated how sentiments toward “vaccination” changed over time. In addition, we analyzed the most retweeted or liked tweets by observing most frequent nouns and sentiments toward key aspects. Results After applying the ABSA system, we obtained 170 aspect terms (eg, “immunity” and “pfizer”) and 6775 opinion terms (eg, “trustworthy” for the positive sentiment and “jeopardize” for the negative sentiment). While manually verifying or editing these terms, our public health experts selected 20 key aspects related to COVID-19 vaccination for analysis. The sentiment analysis results for the 20 key aspects revealed negative sentiments related to “vaccine distribution,” “side effects,” “allergy,” “reactions,” and “anti-vaxxer,” and positive sentiments related to “vaccine campaign,” “vaccine candidates,” and “immune response.” These results indicate that the Twitter users express concerns about the safety of vaccines but still consider vaccines as the option to end the pandemic. In addition, compared to the sentiment of the remaining tweets, the most retweeted or liked tweets showed more positive sentiment overall toward key aspects (P<.001), especially vaccines (P<.001) and vaccination (P=.009). Further investigation of the most retweeted or liked tweets revealed two opposing trends in Twitter users who showed negative sentiments toward vaccines: the “anti-vaxxer” population that used negative sentiments as a means to discourage vaccination and the “Covid Zero” population that used negative sentiments to encourage vaccinations while critiquing the public health response. Conclusions Our study examined public sentiments toward COVID-19 vaccination on tweets over an extended period in Canada. Our findings could inform public health agencies to design and implement interventions to promote vaccination.
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Affiliation(s)
- Hyeju Jang
- Department of Computer Science, University of British Columbia, Vancouver, CA.,British Columbia Centre for Disease Control, Vancouver, CA
| | - Emily Rempel
- British Columbia Centre for Disease Control, Vancouver, CA
| | - Ian Roe
- British Columbia Centre for Disease Control, Vancouver, CA
| | - Prince A Adu
- British Columbia Centre for Disease Control, Vancouver, CA
| | - Giuseppe Carenini
- Department of Computer Science, University of British Columbia, Vancouver, CA
| | - Naveed Zafar Janjua
- British Columbia Centre for Disease Control, Vancouver, CA.,School of Population and Public Health, University of British Columbia, Vancouver, CA.,Centre for Health Evaluation and Outcome Sciences, St. Paul's Hospital, Vancouver, CA
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14
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Ahmad Rizal AR, Nordin SM, Ahmad WFW, Ahmad Khiri MJ, Hussin SH. How Does Social Media Influence People to Get Vaccinated? The Elaboration Likelihood Model of a Person's Attitude and Intention to Get COVID-19 Vaccines. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:2378. [PMID: 35206563 PMCID: PMC8872449 DOI: 10.3390/ijerph19042378] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 02/07/2022] [Accepted: 02/07/2022] [Indexed: 02/05/2023]
Abstract
The global COVID-19 mass vaccination program has created a polemic amongst pro- and anti-vaccination groups on social media. However, the working mechanism on how the shared information might influence an individual decision to be vaccinated is still limited. This study embarks on adopting the elaboration likelihood model (ELM) framework. We examined the function of central route factors (information completeness and information accuracy) as well as peripheral route factors (experience sharing and social pressure) in influencing attitudes towards vaccination and the intention to obtain the vaccine. We use a factorial design to create eight different scenarios in the form of Twitter posts to test the interaction and emulate the situation on social media. In total, 528 respondents were involved in this study. Findings from this study indicated that both the central route and peripheral route significantly influence individually perceived informativeness and perceived persuasiveness. Consequently, these two factors significantly influence attitude towards vaccination and intention to obtain the vaccine. According to the findings, it is suggested that, apart from evidence-based communication, the government or any interested parties can utilize both experience sharing and social pressure elements to increase engagement related to COVID-19 vaccines on social media, such as Twitter.
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Affiliation(s)
- Ammar Redza Ahmad Rizal
- Centre for Research in Media and Communication (MENTION), Faculty of Science Social and Humanities, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Shahrina Md Nordin
- Centre of Social Innovation, Institute of Sustainable Building, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Malaysia; (S.M.N.); (W.F.W.A.)
| | - Wan Fatimah Wan Ahmad
- Centre of Social Innovation, Institute of Sustainable Building, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Malaysia; (S.M.N.); (W.F.W.A.)
| | - Muhammad Jazlan Ahmad Khiri
- Faculty of Language and Communication, Universiti Malaysia Sarawak, Kota Samarahan 94300, Malaysia; (M.J.A.K.); (S.H.H.)
| | - Siti Haslina Hussin
- Faculty of Language and Communication, Universiti Malaysia Sarawak, Kota Samarahan 94300, Malaysia; (M.J.A.K.); (S.H.H.)
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15
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Blane J, Bellutta D, Carley KM. Social-Cyber Maneuvers Analysis During the COVID-19 Vaccine Initial Rollout. J Med Internet Res 2022; 24:e34040. [PMID: 35044302 PMCID: PMC8903203 DOI: 10.2196/34040] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 12/18/2021] [Accepted: 01/08/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND During the time surrounding the approval and initial distribution of Pfizer-BioNTech's COVID-19 vaccine, large numbers of social media users took to used their platforms to voice opinions on the vaccine. They formed pro- and anti-vaccination groups towards the purpose of influencing behaviors to vaccinate or not to vaccinate. The methods of persuasion and manipulation for convincing audiences online can be characterized under a framework for social-cyber maneuvers known as the BEND maneuvers. Previous studies have been conducted on the spread of COVID-19 vaccine disinformation. However, these previous studies lacked comparative analyses over time on both community stances and the competing techniques of manipulating both the narrative and network structure to persuade target audiences. OBJECTIVE This study aimed to understand community response to vaccination by dividing Twitter data from the initial Pfizer-BioNTech COVID-19 vaccine rollout into pro-vaccine and anti-vaccine stances, identifying key actors and groups, and evaluating how the different communities use social-cyber maneuvers, or BEND maneuvers, to influence their target audiences and the network as a whole. METHODS COVID-19 Twitter vaccine data was collected using the Twitter API for one-week periods before, during, and six weeks after the initial Pfizer-BioNTech rollout (December 2020-January 2021). Bot identifications and linguistic cues were derived for users and tweets, respectively, to use as metrics for evaluating social-cyber maneuvers. ORA-PRO software was then used to separate the vaccine data into pro-vaccine and anti-vaccine communities and to facilitate identification of key actors, groups, and BEND maneuvers for a comparative analysis between each community and the entire network. RESULTS Both the pro-vaccine and anti-vaccine communities used combinations of the 16 BEND maneuvers to persuade their target audiences of their particular stances. Our analysis showed how each side attempted to build its own community while simultaneously narrowing and neglecting the opposing community. Pro-vaccine users primarily used positive maneuvers such as excite and explain messages to encourage vaccination and backed leaders within their group. In contrast, anti-vaccine users relied on negative maneuvers to dismay and distort messages with narratives on side effects and death and attempted to neutralize the effectiveness of the leaders within the pro-vaccine community. Furthermore, nuking through platform policies showed to be effective in reducing the size of the anti-vaccine online community and the quantity of anti-vaccine messages. CONCLUSIONS Social media continues to be a domain for manipulating beliefs and ideas. These conversations can ultimately lead to real-world actions such as to vaccinate or not to vaccinate against COVID-19. Moreover, social media policies should be further explored as an effective means for curbing disinformation and misinformation online. CLINICALTRIAL Not applicable.
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Affiliation(s)
- Janice Blane
- School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, US
| | - Daniele Bellutta
- School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, US
| | - Kathleen M Carley
- School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, US
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16
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COVID-19 analytics: Towards the effect of vaccine brands through analyzing public sentiment of tweets. INFORMATICS IN MEDICINE UNLOCKED 2022; 31:100969. [PMID: 35620215 PMCID: PMC9121735 DOI: 10.1016/j.imu.2022.100969] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 05/11/2022] [Accepted: 05/12/2022] [Indexed: 02/08/2023] Open
Abstract
The COVID-19 outbreak has created effects on everyday life worldwide. Many research teams at major pharmaceutical companies and research institutes in various countries have been producing vaccines since the beginning of the outbreak. There is an impact of gender on vaccine responses, acceptance, and outcomes. Worldwide promotion of the COVID-19 vaccine additionally generates a huge amount of discussions on social media platforms about diverse factors of vaccines including protection and efficacy. Twitter is considered one of the most well-known social media platforms which have been widely used to share a public opinion on vaccine-related problems in the COVID-19 pandemic. However, there is a lack of research work to analyze the public perception of COVID-19 vaccines systematically from a gender perspective. In this paper, we perform an in-depth analysis of the coronavirus vaccine-related tweets to understand the people's sentiment towards various vaccine brands corresponding to the gender level. The proposed method focuses on the effect of COVID-19 vaccines on gender by taking into account descriptive, diagnostic, predictive, and prescriptive analytics on the Twitter dataset. We also conduct experiments with deep learning models to determine the sentiment polarities of tweets, which are positive, neutral, and negative. The results reveal that LSTM performs better compared to other models with an accuracy rate of 85.7%.
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17
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Lee CS, Goh DHL, Tan HW, Zheng H, Theng YL. Understanding the Temporal Effects on Tweetcussion of COVID-19 Vaccine. PROCEEDINGS OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY. ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY 2021; 58:768-770. [PMID: 34901402 PMCID: PMC8646292 DOI: 10.1002/pra2.556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In the fight against COVID‐19, the Pfizer and BioNTech vaccine announcement marked a significant turning point. Analysing the topics discussed surrounding the announcement is critical to shed light on how people respond to the vaccination against COVID‐19. Specifically, since the COVID‐19 vaccine was developed at unprecedented speed, different segments of the public with a different understanding of the issues may react and respond differently. We analysed Twitter tweets to uncover the issues surrounding people's discussion of the vaccination against COVID‐19. Through the use of Latent Dirichlet Allocation (LDA), nine topics were identified pertaining to vaccine‐related tweets. We analysed the temporal differences in the nine topics, prior and after the official vaccine announcement.
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18
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Jarynowski A, Semenov A, Kamiński M, Belik V. Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning. J Med Internet Res 2021; 23:e30529. [PMID: 34662291 PMCID: PMC8631420 DOI: 10.2196/30529] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 08/12/2021] [Accepted: 09/28/2021] [Indexed: 02/06/2023] Open
Abstract
Background There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs. Objective We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs. Methods We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multilabel classifications using the deep neural language model Bidirectional Encoder Representations from Transformers (BERT) “DeepPavlov,” which was pretrained on a Russian language corpus and applied to the Telegram messages. The resulting area under the curve score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea. Results Telegram users complained mostly about pain (5461/11,515, 47.43%), fever (5363/11,515, 46.57%), fatigue (3862/11,515, 33.54%), and headache (2855/11,515, 24.79%). Women reported more AEs than men (1.2-fold, P<.001). In addition, there were more AEs from the first dose than from the second dose (1.1-fold, P<.001), and the number of AEs decreased with age (β=.05 per year, P<.001). The results also showed that Sputnik V AEs were more similar to other vector vaccines (132 units) than with messenger RNA vaccines (241 units) according to the average Euclidean distance between the vectors of AE frequencies. Elderly Telegram users reported significantly more (5.6-fold on average) systemic AEs than their peers, according to the results of the phase 3 clinical trials published in The Lancet. However, the AEs reported in Telegram posts were consistent (Pearson correlation r=0.94, P=.02) with those reported in the Argentinian postmarketing AE registry. Conclusions After the Sputnik V vaccination, Russian Telegram users reported mostly pain, fever, and fatigue. The Sputnik V AE profile was comparable with other vector COVID-19 vaccines. Discussion on social media could provide meaningful information about the AE profile of novel vaccines.
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Affiliation(s)
- Andrzej Jarynowski
- System Modeling Group, Institute for Veterinary Epidemiology and Biostatistics, Freie Universität Berlin, Berlin, Germany.,Interdisciplinary Research Institute, Wrocław/Głogów, Poland
| | - Alexander Semenov
- Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, United States.,Center for Econometrics and Business Analytics, St. Petersburg State University, Saint Petersburg, Russian Federation
| | | | - Vitaly Belik
- System Modeling Group, Institute for Veterinary Epidemiology and Biostatistics, Freie Universität Berlin, Berlin, Germany
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19
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Unmasking People’s Opinions behind Mask-Wearing during COVID-19 Pandemic—A Twitter Stance Analysis. Symmetry (Basel) 2021. [DOI: 10.3390/sym13111995] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Wearing a mask by the general public has been a controversial issue from the beginning of the COVID-19 pandemic as the public authorities have had mixed messages, either advising people not to wear masks if uninfected, to wear as a protective measure, to wear them only when inside a building/room with insufficient air flow or to wear them in all the public places. To date, the governments have had different policies regarding mask-wearing by the general public depending on the COVID-19 pandemic evolution. In this context, the paper analyzes the general public’s opinion regarding mask-wearing for the one-year period starting from 9 January 2020, when the first tweet regarding mask-wearing in the COVID-19 context has been posted. Classical machine learning and deep learning algorithms have been considered in analyzing the 8,795,633 tweets extracted. A random sample of 29,613 tweets has been extracted and annotated. The tweets containing news and information related to mask-wearing have been included in the neutral category, while the ones containing people’s opinions (for or against) have been marked using a symmetrical approach into in favor and against categories. Based on the analysis, it has been determined that most of the mask tweets are in the area of in favor or neutral, while a smaller percentage of tweets and retweets are in the against category. The evolution of the opinions expressed through tweets can be further monitored for extracting the public perspective on mask-wearing in times of COVID-19.
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20
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Public perception of COVID-19 vaccines from the digital footprints left on Twitter: analyzing positive, neutral and negative sentiments of Twitterati. LIBRARY HI TECH 2021. [DOI: 10.1108/lht-08-2021-0261] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeTwitter is gaining popularity as a microblogging and social networking service to discuss various social issues. Coronavirus disease 2019 (COVID-19) has become a global pandemic and is discussed worldwide. Social media is an instant platform to deliberate various dimensions of COVID-19. The purpose of the study is to explore and analyze the public sentiments related to COVID-19 vaccines across the Twitter messages (positive, neutral, and negative) and the impact tweets make across digital social circles.Design/methodology/approachTo fetch the vaccine-related posts, a manual examination of randomly selected 500 tweets was carried out to identify the popular hashtags relevant to the vaccine conversation. It was found that the hashtags “covid19vaccine” and “coronavirusvaccine” were the two popular hashtags used to discuss the communications related to COVID-19 vaccines. 23,575 global tweets available in public domain were retrieved through “Twitter Application Programming Interface” (API), using “Orange Software”, an open-source machine learning, data visualization and data mining toolkit. The study was confined to the tweets posted in English language only. The default data cleaning and preprocessing techniques available in the “Orange Software” were applied to the dataset, which include “transformation”, “tokenization” and “filtering”. The “Valence Aware Dictionary for sEntiment Reasoning” (VADER) tool was used for classification of tweets to determine the tweet sentiments (positive, neutral and negative) as well as the degree of sentiments (compound score also known as sentiment score). To assess the influence/impact of tweets account wise (verified and unverified) and sentiment wise (positive, neutral, and negative), the retweets and likes, which offer a sort of reward or acknowledgment of tweets, were used.FindingsA gradual decline in the number of tweets over the time is observed. Majority (11,205; 47.52%) of tweets express positive sentiments, followed by neutral (7,948; 33.71%) and negative sentiments (4,422; 18.75%), respectively. The study also signifies a substantial difference between the impact of tweets tweeted by verified and unverified users. The tweets related to verified users have a higher impact both in terms of retweets (65.91%) and likes (84.62%) compared to the tweets tweeted by unverified users. Tweets expressing positive sentiments have the highest impact both in terms of likes (mean = 10.48) and retweets (mean = 3.07) compared to those that express neutral or negative sentiments.Research limitations/implicationsThe main limitation of the study is that the sentiments of the people expressed over one single social platform, that is, Twitter have been studied which cannot generalize the global public perceptions. There can be a variation in the results when the datasets from other social media platforms will be studied.Practical implicationsThe study will help to know the people's sentiments and beliefs toward the COVID-19 vaccines. Sentiments that people hold about the COVID-19 vaccines are studied, which will help health policymakers understand the polarity (positive, negative, and neutral) of the tweets and thus see the public reaction and reflect the types of information people are exposed to about vaccines. The study can aid the health sectors to intensify positive messages and eliminate negative messages for an enhanced vaccination uptake. The research can also help design more operative vaccine-advocating communication by customizing messages using the obtained knowledge from the sentiments and opinions about the vaccines.Originality/valueThe paper focuses on an essential aspect of COVID-19 vaccines and how people express themselves (positively, neutrally and negatively) on Twitter.
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21
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COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter. Vaccines (Basel) 2021; 9:vaccines9101059. [PMID: 34696167 PMCID: PMC8540945 DOI: 10.3390/vaccines9101059] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 09/06/2021] [Accepted: 09/16/2021] [Indexed: 01/18/2023] Open
Abstract
The understanding of the public response to COVID-19 vaccines is the key success factor to control the COVID-19 pandemic. To understand the public response, there is a need to explore public opinion. Traditional surveys are expensive and time-consuming, address limited health topics, and obtain small-scale data. Twitter can provide a great opportunity to understand public opinion regarding COVID-19 vaccines. The current study proposes an approach using computational and human coding methods to collect and analyze a large number of tweets to provide a wider perspective on the COVID-19 vaccine. This study identifies the sentiment of tweets using a machine learning rule-based approach, discovers major topics, explores temporal trend and compares topics of negative and non-negative tweets using statistical tests, and discloses top topics of tweets having negative and non-negative sentiment. Our findings show that the negative sentiment regarding the COVID-19 vaccine had a decreasing trend between November 2020 and February 2021. We found Twitter users have discussed a wide range of topics from vaccination sites to the 2020 U.S. election between November 2020 and February 2021. The findings show that there was a significant difference between tweets having negative and non-negative sentiment regarding the weight of most topics. Our results also indicate that the negative and non-negative tweets had different topic priorities and focuses. This research illustrates that Twitter data can be used to explore public opinion regarding the COVID-19 vaccine.
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