ReviewInternational journal of environmental research and public health2022
Health Misinformation Detection in the Social Web: An Overview and a Data Science Approach.
Review in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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
24 citing papers in PubMed.
- Exploring Conversational Dynamics in Scientific and Pseudoscientific Health Communities on YouTube: Process Mining and Network Analysis Study.Journal of medical Internet research · 2026Article
- Comparative analysis of early and late recurrence rates in perianal fistula surgery.Experimental and therapeutic medicine · 2026Article
- Communicating Risk in the Age of Misinformation: Empirical Frameworks for Understanding Stakeholder Trust.Risk analysis : an official publication of the Society for Risk Analysis · 2026Article
- Enhancing trustworthiness of Arabic online health information quality evaluation using an enhanced BERT architecture with PCA and ICA feature weighting.Scientific reports · 2026Article
- Addressing misleading medical information on social media: a scoping review of current interventions.BMJ evidence-based medicine · 2025Article
- Artificial Intelligence and Digital Technologies Against Health Misinformation: A Scoping Review of Public Health Responses.Healthcare (Basel, Switzerland) · 2025Review
- Survey on Chinese users' acceptance of AI assistants: expanding technology acceptance model.Scientific reports · 2025Article
- Access to healthy ınformatıon: the ınteractıon of medıa lıteracy and health lıteracy.BMC public health · 2025Article
- Analysing health misinformation with advanced centrality metrics in online social networks.PLOS digital health · 2025Article
- Use of Retrieval-Augmented Large Language Model for COVID-19 Fact-Checking: Development and Usability Study.Journal of medical Internet research · 2025Article
- Racial and Demographic Disparities in Susceptibility to Health Misinformation on Social Media: National Survey-Based Analysis.Journal of medical Internet research · 2024Article
- Article
- Educational Video Intervention to Improve Health Misinformation Identification on WhatsApp Among Saudi Arabian Population: Pre-Post Intervention Study.JMIR formative research · 2024Article
- Public Health Policy, Political Ideology, and Public Emotion Related to COVID-19 in the U.S.International journal of environmental research and public health · 2023Article
- Knowledge and attitude toward oral health behavior of overseas students during the COVID-19 pandemic.BMC oral health · 2023Article
- Automatic detection of health misinformation: a systematic review.Journal of ambient intelligence and humanized computing · 2023Article
- Effects of Visual Communication Design Accessibility (VCDA) Guidelines for Low Vision on Public and Open Government Health Data.Healthcare (Basel, Switzerland) · 2023Article
- A survey of uncover misleading and cyberbullying on social media for public health.Cluster computing · 2023Review
- Explainable online health information truthfulness in Consumer Health Search.Frontiers in artificial intelligence · 2023Article
- Detecting and classifying online health misinformation with 'Content Similarity Measure (CSM)' algorithm: an automated fact-checking-based approach.The Journal of supercomputing · 2023Article
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
The increasing availability of online content these days raises several questions about effective access to information. In particular, the possibility for almost everyone to generate content with no traditional intermediary, if on the one hand led to a process of "information democratization", on the other hand, has negatively affected the genuineness of the information disseminated. This issue is particularly relevant when accessing health information, which impacts both the individual and societal level. Often, laypersons do not have sufficient health literacy when faced with the decision to rely or not rely on this information, and expert users cannot cope with such a large amount of content. For these reasons, there is a need to develop automated solutions that can assist both experts and non-experts in discerning between genuine and non-genuine health information. To make a contribution in this area, in this paper we proceed to the study and analysis of distinct groups of features and machine learning techniques that can be effective to assess misinformation in online health-related content, whether in the form of Web pages or social media content. To this aim, and for evaluation purposes, we consider several publicly available datasets that have only recently been generated for the assessment of health misinformation under different perspectives.
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.