ArticlePloS one2020
Comparing covariation among vaccine hesitancy and broader beliefs within Twitter and survey data.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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
9 citing papers in PubMed.
- Understanding Health-Related Discussions on Reddit: Development of a Topic Assignment Method and Exploratory Analysis.JMIR formative research · 2025Article
- Conspiracy narratives and vaccine hesitancy: a scoping review of prevalence, impact, and interventions.BMC public health · 2024Article
- Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media.PloS one · 2024Article
- Messaging Strategies for Mitigating COVID-19 Through Vaccination and Nonpharmaceutical Interventions.Rand health quarterly · 2022Article
- Social media and attitudes towards a COVID-19 vaccination: A systematic review of the literature.EClinicalMedicine · 2022Article
- Facilitators and Barriers of COVID-19 Vaccine Promotion on Social Media in the United States: A Systematic Review.Healthcare (Basel, Switzerland) · 2022Review
- Polarization and trust in the evolution of vaccine discourse on Twitter during COVID-19.PloS one · 2022Observational
- COVID-19 Vaccine Hesitancy in Canada: Content Analysis of Tweets Using the Theoretical Domains Framework.Journal of medical Internet research · 2021Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Over the past decade, the percentage of adults in the United States who use some form of social media has roughly doubled, increasing from 36 percent in early 2009 to 72 percent in 2019. There has been a corresponding increase in research aimed at understanding opinions and beliefs that are expressed online. However, the generalizability of findings from social media research is a subject of ongoing debate. Social media platforms are conduits of both information and misinformation about vaccines and vaccine hesitancy. Our research objective was to examine whether we can draw similar conclusions from Twitter and national survey data about the relationship between vaccine hesitancy and a broader set of beliefs. In 2018 we conducted a nationally representative survey of parents in the United States informed by a literature review to ask their views on a range of topics, including vaccine side effects, conspiracy theories, and understanding of science. We developed a set of keyword-based queries corresponding to each of the belief items from the survey and pulled matching tweets from 2017. We performed the data pull of the most recent full year of data in 2018. Our primary measures of belief covariation were the loadings and scores of the first principal components obtained using principal component analysis (PCA) from the two sources. We found that, after using manually coded weblinks in tweets to infer stance, there was good qualitative agreement between the first principal component loadings and scores using survey and Twitter data. This held true after we took the additional processing step of resampling the Twitter data based on the number of topics that an individual tweeted about, as a means of correcting for differential representation for elicited (survey) vs. volunteered (Twitter) beliefs. Overall, the results show that analyses using Twitter data may be generalizable in certain contexts, such as assessing belief covariation.
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What OpenQuestion holds
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.