ArticleJournal of ambient intelligence and humanized computing2023
Automatic detection of health misinformation: a systematic review.
Article in Journal of ambient intelligence and humanized computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Systematic Review of Features Forecasting Patient Arrival Numbers.Computers, informatics, nursing : CIN · 2025Pooled it
- Sources of information on monkeypox virus infection. A systematic review with meta-analysis.BMC public health · 2024Pooled it
- AI-Augmented Prevention Science Needs Community-Engaged Prevention Science: a Framework for Greater Accountability.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Development and validation of a tool for detecting misinformation risk in diet, nutrition, and health content (Diet-MisRAT).Scientific reports · 2026Article
- Article
- Promoting informed health choices: the long and winding road.Journal of the Royal Society of Medicine · 2025Article
- A scoping review of natural language processing in addressing medically inaccurate information: Errors, misinformation, and hallucination.Journal of biomedical informatics · 2025Article
- Classifying and fact-checking health-related information about COVID-19 on Twitter/X using machine learning and deep learning models.BMC medical informatics and decision making · 2025Article
- An overview of fake news detection: From a new perspective.Fundamental research · 2025Review
- Analysis of child development facts and myths using text mining techniques and classification models.Heliyon · 2024Article
- Health Misinformation Detection: Approaches, Challenges and Opportunities.Inquiry : a journal of medical care organization, provision and financingReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The spread of health misinformation has the potential to cause serious harm to public health, from leading to vaccine hesitancy to adoption of unproven disease treatments. In addition, it could have other effects on society such as an increase in hate speech towards ethnic groups or medical experts. To counteract the sheer amount of misinformation, there is a need to use automatic detection methods. In this paper we conduct a systematic review of the computer science literature exploring text mining techniques and machine learning methods to detect health misinformation. To organize the reviewed papers, we propose a taxonomy, examine publicly available datasets, and conduct a content-based analysis to investigate analogies and differences among Covid-19 datasets and datasets related to other health domains. Finally, we describe open challenges and conclude with future directions.
Indexed as
Identifiers
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.