ArticleJMIR infodemiology
Examining the Public's Most Frequently Asked Questions Regarding COVID-19 Vaccines Using Search Engine Analytics in the United States: Observational Study.
Article in JMIR infodemiology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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.
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Who cites it
18 citing papers in PubMed.
- What are patients asking and reading online? An analysis of online patient searches about treatments for developmental dysplasia of the hip.Journal of children's orthopaedics · 2025Article
- Modern Digital Query Analytics of Patient Education Materials on Acanthosis Nigricans: Systematic Search and Content Analysis.JMIR dermatology · 2025Article
- Article
- Gene editing in animals: What does the public want to know and what information do stakeholder organizations provide?Public understanding of science (Bristol, England) · 2024Article
- Leveraging Generative Artificial Intelligence Models in Patient Education on Inferior Vena Cava Filters.Clinics and practice · 2024Article
- Examining the Public's Most Frequently Asked Questions About Carpal Tunnel Syndrome and Appraising Online Information About Treatment.Hand (New York, N.Y.) · 2024Article
- Article
- Digital Patient Education on Xanthelasma Palpebrarum: A Content Analysis.Clinics and practice · 2023Article
- Insights Into Patients Questions Over Bunion Treatments: A Google Study.Foot & ankle orthopaedics · 2023Article
- Modern internet search analytics and spine: what are patients asking and reading online?North American Spine Society journal · 2023Article
- Internet search analysis on the treatment of rheumatoid arthritis: What do people ask and read online?PloS one · 2023Article
- Health Literacy Analytics of Accessible Patient Resources in Cardiovascular Medicine: What are Patients Wanting to Know?Kansas journal of medicine · 2023Article
- COVID-19 vaccine-related frequently asked questions (FAQs) by people with epilepsy and carers in Iran; educational video is included.Epilepsy & behavior : E&B · 2022Article
- Content and Dynamics of Websites Shared Over Vaccine-Related Tweets in COVID-19 Conversations: Computational Analysis.Journal of medical Internet research · 2021Article
- Media Data and Vaccine Hesitancy: Scoping Review.JMIR infodemiologyArticle
- Enhancing Patient Education on Cardiovascular Rehabilitation with Large Language Models.Missouri medicineArticle
- Article
- Integrating Google Trends Search Engine Query Data Into Adult Emergency Department Volume Forecasting: Infodemiology Study.JMIR infodemiologyArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe emergency authorization of COVID-19 vaccines has offered the first means of long-term protection against COVID-19-related illness since the pandemic began. It is important for health care professionals to understand commonly held COVID-19 vaccine concerns and to be equipped with quality information that can be used to assist in medical decision-making.
objectiveUsing Google's RankBrain machine learning algorithm, we sought to characterize the content of the most frequently asked questions (FAQs) about COVID-19 vaccines evidenced by internet searches. Secondarily, we sought to examine the information transparency and quality of sources used by Google to answer FAQs on COVID-19 vaccines.
methodsWe searched COVID-19 vaccine terms on Google and used the "People also ask" box to obtain FAQs generated by Google's machine learning algorithms. FAQs are assigned an "answer" source by Google. We extracted FAQs and answer sources related to COVID-19 vaccines. We used the Rothwell Classification of Questions to categorize questions on the basis of content. We classified answer sources as either academic, commercial, government, media outlet, or medical practice. We used the Journal of the American Medical Association's (JAMA's) benchmark criteria to assess information transparency and Brief DISCERN to assess information quality for answer sources. FAQ and answer source type frequencies were calculated. Chi-square tests were used to determine associations between information transparency by source type. One-way analysis of variance was used to assess differences in mean Brief DISCERN scores by source type.
resultsOur search yielded 28 unique FAQs about COVID-19 vaccines. Most COVID-19 vaccine-related FAQs were seeking factual information (22/28, 78.6%), specifically about safety and efficacy (9/22, 40.9%). The most common source type was media outlets (12/28, 42.9%), followed by government sources (11/28, 39.3%). Nineteen sources met 3 or more JAMA benchmark criteria with government sources as the majority (10/19, 52.6%). JAMA benchmark criteria performance did not significantly differ among source types (
conclusionsThe most frequently asked COVID-19 vaccine-related questions pertained to vaccine safety and efficacy. We found that government sources provided the most transparent and highest-quality web-based COVID-19 vaccine-related information. Recognizing common questions and concerns about COVID-19 vaccines may assist in improving vaccination efforts.
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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.