ArticleJournal of medical Internet research2021
Understanding Behavioral Intentions Toward COVID-19 Vaccines: Theory-Based Content Analysis of Tweets.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Misinformation About COVID-19 Vaccines on Social Media: Rapid Review.Journal of medical Internet research · 2022Pooled it
- Vaccine hesitancy and decision regret among nurses in oncology settings in Italy: a cluster-based profile analysis.Scientific reports · 2025Article
- Willingness to Pay for the COVID-19 Vaccine and Its Correlates in Bangladesh: Cross-Sectional Study.JMIRx med · 2025Article
- Boost your health (Refuerza tu Salud): Design of a randomized controlled trial of a community health worker intervention to reduce inequities in COVID-19 and influenza vaccinations.Contemporary clinical trials · 2025Article
- COVID-19 Public Health Communication on X (Formerly Twitter): Cross-Sectional Study of Message Type, Sentiment, and Source.JMIR formative research · 2025Article
- Analyzing information sharing behaviors during stance formation on COVID-19 vaccination among Japanese Twitter users.PloS one · 2024Article
- Uptake of COVID-19 vaccine among high-risk urban populations in Southern Thailand using the COM-B model.PloS one · 2024Article
- Longitudinal analysis of behavioral factors and techniques used to identify vaccine hesitancy among Twitter users: Scoping review.Human vaccines & immunotherapeutics · 2023Article
- Extracting factors associated with vaccination from Twitter data and mapping to behavioral models.Human vaccines & immunotherapeutics · 2023Article
- Conditions for successful implementation of couple-based collaborative management model of diabetes among community-dwelling older Chinese: a qualitative comparative analysis.BMC geriatrics · 2023Article
- Predictors of changing patterns of adherence to containment measures during the early stage of COVID-19 pandemic: an international longitudinal study.Globalization and health · 2023Article
- Exploring the Influencing Factors of COVID-19 Vaccination Willingness among Young Adults in China.International journal of environmental research and public health · 2023Article
- Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review.Journal of medical Internet research · 2023Article
- Psychometric development of the COVID-19 vaccine misinformation scale and effects on vaccine hesitancy.Preventive medicine reports · 2023Article
- The predictors of the intention to receive Covid-19 vaccine using the health belief model and theory of planned behavior in South Khorasan province.Journal of education and health promotion · 2023Article
- The mediating role of product judgment and country of origin effect on health literacy and behavioral intention: A study on COVID-19 vaccines perception of Turkish consumers.Human vaccines & immunotherapeutics · 2022Article
- Social media and attitudes towards a COVID-19 vaccination: A systematic review of the literature.EClinicalMedicine · 2022Article
- Predicting the COVID-19 vaccine receive intention based on the theory of reasoned action in the south of Iran.BMC public health · 2022Article
- Examining the Utility of Social Media in COVID-19 Vaccination: Unsupervised Learning of 672,133 Twitter Posts.JMIR public health and surveillance · 2021Article
- COVID-19 Vaccine and Social Media in the U.S.: Exploring Emotions and Discussions on Twitter.Vaccines · 2021Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcceptance rates of COVID-19 vaccines have still not reached the required threshold to achieve herd immunity. Understanding why some people are willing to be vaccinated and others are not is a critical step to develop efficient implementation strategies to promote COVID-19 vaccines.
objectiveWe conducted a theory-based content analysis based on the capability, opportunity, motivation-behavior (COM-B) model to characterize the factors influencing behavioral intentions toward COVID-19 vaccines mentioned on the Twitter platform.
methodsWe collected tweets posted in English from November 1-22, 2020, using a combination of relevant keywords and hashtags. After excluding retweets, we randomly selected 5000 tweets for manual coding and content analysis. We performed a content analysis informed by the adapted COM-B model.
resultsOf the 5000 COVID-19 vaccine-related tweets that were coded, 4796 (95.9%) were posted by unique users. A total of 97 tweets carried positive behavioral intent, while 182 tweets contained negative behavioral intent. Of these, 28 tweets were mapped to capability factors, 155 tweets were related to motivation, 23 tweets were related to opportunities, and 74 tweets did not contain any useful information about the reasons for their behavioral intentions (κ=0.73). Some tweets mentioned two or more constructs at the same time. Tweets that were mapped to capability (P<.001), motivation (P<.001), and opportunity (P=.03) factors were more likely to indicate negative behavioral intentions.
conclusionsMost behavioral intentions regarding COVID-19 vaccines were related to the motivation construct. The themes identified in this study could be used to inform theory-based and evidence-based interventions to improve acceptance of COVID-19 vaccines.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.