SynthesisAmerican journal of public health2017
Twitter as a Tool for Health Research: A Systematic Review.
Synthesis in American journal of public health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05442762 (A Social Media-based Machine Learning Study to Monitor Vaccine Confidence and Hesitancy and Early Warn Emerging Vaccine-related Risks in Real Time), which is not on this map. Cited by 310 papers, 7 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
A Social Media-based Machine Learning Study to Monitor Vaccine Confidence and Hesitancy and Early Warn Emerging Vaccine-related Risks in Real Time
Who cites it
310 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Acute respiratory infections prediction through social media data and machine learning: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Social media for palliative and end-of-life care research: a systematic review.BMJ supportive & palliative care · 2024Pooled it
- Innovating health prevention models in detecting infectious disease outbreaks through social media data: an umbrella review of the evidence.Frontiers in public health · 2024Pooled it
- Occupational Health Applied Infodemiological Studies of Nutritional Diseases and Disorders: Scoping Review with Meta-Analysis.Nutrients · 2023Pooled it
- Ethical and Methodological Considerations of Twitter Data for Public Health Research: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Social Media Use for Health Purposes: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Machine Learning and Natural Language Processing in Mental Health: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Effect of Posting on Social Media on Systolic Blood Pressure and Management of Hypertension: A Randomized Controlled Trial.Journal of the American Heart Association · 2021Trial
- "Unmasking Misinformation": Characterizing the Social Media Landscape of Disorders of Consciousness on X (formerly Twitter).Research square · 2026Article
- Investigating Online Discussions About Cancer Screening on Twitter (Subsequently Rebranded as X): Corpus Analysis.JMIR infodemiology · 2026Article
- Traditional, Complementary, and Integrative Medicine on X (Formerly Twitter) From 2015 to 2024 Across English, Spanish, and French: Retrospective Observational Study.Journal of medical Internet research · 2026Observational
- Harnessing AI and social media to understand real-world patient experiences in SLE.Lupus science & medicine · 2026Article
- Deep learning analysis of trajectories and spatial variations in HPV vaccine discussions on Chinese Weibo.Communications medicine · 2026Article
- Public Views on Pesticide Exposure and Human Biomonitoring in Latvia: Evidence from Focus Groups and Media Analysis.Toxics · 2026Article
- Self-Reported Health Outcomes in Metabolic Health YouTube Comments: Cross-Sectional Study and Rule-Based Natural Language Processing Framework Development and Validation.Journal of medical Internet research · 2026Observational
- Thematic Mapping and Evolution of Social Media Mining in Health Research: Hybrid Bibliometric Synthesis.Journal of medical Internet research · 2026Review
- The Digital Information Environment of Lung Cancer and Lung Cancer Screening: Protocol for a Cross-Platform Social Media Content Analysis.JMIR research protocols · 2026Article
- Examining contraception-related discourse on social media after the Dobbs v. Jackson Women's Health Organization Supreme Court decision: a textual analysis of user-generated content on X (formerly Twitter).Reproductive health · 2026Article
- Digital phenotyping and ASCVD risk: An exploratory cross-sectional analysis using online behavioral data.American heart journal plus : cardiology research and practice · 2026Article
- Sentiment and emotion analysis of dental pain posts on X (formerly Twitter).The Saudi dental journal · 2026Article
250 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
6 authors.
Funding
Abstract
backgroundResearchers have used traditional databases to study public health for decades. Less is known about the use of social media data sources, such as Twitter, for this purpose.
objectivesTo systematically review the use of Twitter in health research, define a taxonomy to describe Twitter use, and characterize the current state of Twitter in health research. SEARCH
methodsWe performed a literature search in PubMed, Embase, Web of Science, Google Scholar, and CINAHL through September 2015. SELECTION CRITERIA: We searched for peer-reviewed original research studies that primarily used Twitter for health research. DATA COLLECTION AND ANALYSIS: Two authors independently screened studies and abstracted data related to the approach to analysis of Twitter data, methodology used to study Twitter, and current state of Twitter research by evaluating time of publication, research topic, discussion of ethical concerns, and study funding source. MAIN
resultsOf 1110 unique health-related articles mentioning Twitter, 137 met eligibility criteria. The primary approaches for using Twitter in health research that constitute a new taxonomy were content analysis (56%; n = 77), surveillance (26%; n = 36), engagement (14%; n = 19), recruitment (7%; n = 9), intervention (7%; n = 9), and network analysis (4%; n = 5). These studies collectively analyzed more than 5 billion tweets primarily by using the Twitter application program interface. Of 38 potential data features describing tweets and Twitter users, 23 were reported in fewer than 4% of the articles. The Twitter-based studies in this review focused on a small subset of data elements including content analysis, geotags, and language. Most studies were published recently (33% in 2015). Public health (23%; n = 31) and infectious disease (20%; n = 28) were the research fields most commonly represented in the included studies. Approximately one third of the studies mentioned ethical board approval in their articles. Primary funding sources included federal (63%), university (13%), and foundation (6%).
conclusionsWe identified a new taxonomy to describe Twitter use in health research with 6 categories. Many data elements discernible from a user's Twitter profile, especially demographics, have been underreported in the literature and can provide new opportunities to characterize the users whose data are analyzed in these studies. Twitter-based health research is a growing field funded by a diversity of organizations. Public health implications. Future work should develop standardized reporting guidelines for health researchers who use Twitter and policies that address privacy and ethical concerns in social media research.
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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.