ArticleJMIR public health and surveillance2019
Google Trends in Infodemiology and Infoveillance: Methodology Framework.
Article in JMIR public health and surveillance, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 226 papers.
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Who cites it
226 citing papers in PubMed.
- Public information-seeking about orthopedic surgical robotics: a decade-scale infodemiological analysis and forecast through 2030.Journal of robotic surgery · 2026Article
- Quality and Readability of Four AI Chatbots Answering Search-Derived Patient Questions About Primary Aldosteronism: A Cross-Sectional Comparative Study.Healthcare (Basel, Switzerland) · 2026Article
- Article
- Modeling infodemics on a global scale: A 30 countries study using epidemiological and social listening data.PNAS nexus · 2026Article
- Performance of Large Language Models in Oral Cancer Patient Education: An Evaluation of Reliability, Readability, and Patient Communication Quality.Oral health & preventive dentistry · 2026Article
- Infodemiology of West Nile Virus in Greece, 2024-2025, with Descriptive One Health Preparedness Evidence from Crete.Epidemiologia (Basel, Switzerland) · 2026Article
- Climate, Humidity, and Population-Level Interest in Dry Skin: Infodemiology Analysis Using Google Trends Across the United States.JMIR dermatology · 2026Article
- Are AI chatbots ready for chikungunya public education? Evidence on validity, reliability, and readability.BMC public health · 2026Article
- Public Interest in Janus Kinase (JAK) Inhibitors for Alopecia Areata: A Google Trend Analysis.JMIR dermatology · 2026Article
- COJMIR medical informatics · 2026Article
- Article
- Insights Into Retrograde Cricopharyngeus Dysfunction (R-CPD) Through Analysis of Internet Search Pattern.Laryngoscope investigative otolaryngology · 2026Article
- Effects of the pandemic and economic crisis on public search trends related to oral and maxillofacial surgery in Türkiye: a Google Trends time-series analysis (2020-2025).BMC oral health · 2026Observational
- Sexual and reproductive health consequences of COVID-19 pandemic in Nigeria: an infodemiological survey.Scientific reports · 2026Article
- Monitoring Public Health During the 2023 Maui Wildfire Using Google Search Trends.Hawai'i journal of health & social welfare · 2026Article
- Evaluating the accuracy, reliability, and readability of AI chatbots in delivering postpartum depression information.Frontiers in psychiatry · 2026Article
- Global information-seeking behavior of air pollution and cardiovascular disease: insights from google trends analysis.Frontiers in epidemiology · 2026Article
- Digital epidemiology and public health surveillance: scientometric mapping of emerging technologies and challenges (2000-2025).Frontiers in digital health · 2026Review
- Evaluation of validity, reliability, and readability of AI chatbots for gestational diabetes mellitus: a multi-model comparative study.Frontiers in public health · 2026Article
- Artificial Intelligence Chatbots as Sources of Cancer Pain Information: A Comparative Evaluation of Quality, Transparency, and Readability.Journal of pain research · 2026Article
166 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Internet data are being increasingly integrated into health informatics research and are becoming a useful tool for exploring human behavior. The most popular tool for examining online behavior is Google Trends, an open tool that provides information on trends and the variations of online interest in selected keywords and topics over time. Online search traffic data from Google have been shown to be useful in analyzing human behavior toward health topics and in predicting disease occurrence and outbreaks. Despite the large number of Google Trends studies during the last decade, the literature on the subject lacks a specific methodology framework. This article aims at providing an overview of the tool and data and at presenting the first methodology framework in using Google Trends in infodemiology and infoveillance, including the main factors that need to be taken into account for a strong methodology base. We provide a step-by-step guide for the methodology that needs to be followed when using Google Trends and the essential aspects required for valid results in this line of research. At first, an overview of the tool and the data are presented, followed by an analysis of the key methodological points for ensuring the validity of the results, which include selecting the appropriate keyword(s), region(s), period, and category. Overall, this article presents and analyzes the key points that need to be considered to achieve a strong methodological basis for using Google Trends data, which is crucial for ensuring the value and validity of the results, as the analysis of online queries is extensively integrated in health research in the big data era.
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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.