ArticleJournal of medical Internet research2021
Using Machine Learning-Based Approaches for the Detection and Classification of Human Papillomavirus Vaccine Misinformation: Infodemiology Study of Reddit Discussions.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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.
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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quality of Cancer-Related Information on New Media (2014-2023): Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Trial
- Deep learning analysis of trajectories and spatial variations in HPV vaccine discussions on Chinese Weibo.Communications medicine · 2026Article
- Human Papillomavirus Vaccine Discourse and Sentiment on Reddit Before and After COVID-19: Mixed Methods Retrospective Cross-Sectional Study.Journal of medical Internet research · 2026Article
- Intervention in Health Misinformation Using Large Language Models for Automated Detection, Thematic Analysis, and Inoculation: Case Study on COVID-19.Journal of medical Internet research · 2026Article
- Demographic disparities, temporal trends, and geographic patterns of HPV vaccination on Long Island, New York: A comprehensive analysis of immunization registry data (2012-2023).Human vaccines & immunotherapeutics · 2025Article
- Artificial Intelligence and Digital Technologies Against Health Misinformation: A Scoping Review of Public Health Responses.Healthcare (Basel, Switzerland) · 2025Review
- A scoping review of natural language processing in addressing medically inaccurate information: Errors, misinformation, and hallucination.Journal of biomedical informatics · 2025Article
- Quality and Misinformation About Health Conditions in Online Peer Support Groups: Scoping Review.Journal of medical Internet research · 2025Article
- The Role of Internet Information on Anti-HPV Vaccines: A Comprehensive Overview of a Double-Edged Sword.Vaccines · 2025Review
- Detecting and monitoring concerns against HPV vaccination on social media using large language models.Scientific reports · 2024Article
- Automatic detection of health misinformation: a systematic review.Journal of ambient intelligence and humanized computing · 2023Article
- Disparities in awareness of the HPV vaccine and HPV-associated cancers among racial/ethnic minority populations: 2018 HINTS.Ethnicity & health · 2023Article
- 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
9 authors.
Funding
Abstract
backgroundThe rapid growth of social media as an information channel has made it possible to quickly spread inaccurate or false vaccine information, thus creating obstacles for vaccine promotion.
objectiveThe aim of this study is to develop and evaluate an intelligent automated protocol for identifying and classifying human papillomavirus (HPV) vaccine misinformation on social media using machine learning (ML)-based methods.
methodsReddit posts (from 2007 to 2017, N=28,121) that contained keywords related to HPV vaccination were compiled. A random subset (2200/28,121, 7.82%) was manually labeled for misinformation and served as the gold standard corpus for evaluation. A total of 5 ML-based algorithms, including a support vector machine, logistic regression, extremely randomized trees, a convolutional neural network, and a recurrent neural network designed to identify vaccine misinformation, were evaluated for identification performance. Topic modeling was applied to identify the major categories associated with HPV vaccine misinformation.
resultsA convolutional neural network model achieved the highest area under the receiver operating characteristic curve of 0.7943. Of the 28,121 Reddit posts, 7207 (25.63%) were classified as vaccine misinformation, with discussions about general safety issues identified as the leading type of misinformed posts (2666/7207, 36.99%).
conclusionsML-based approaches are effective in the identification and classification of HPV vaccine misinformation on Reddit and may be generalizable to other social media platforms. ML-based methods may provide the capacity and utility to meet the challenge involved in intelligent automated monitoring and classification of public health misinformation on social media platforms. The timely identification of vaccine misinformation on the internet is the first step in misinformation correction and vaccine promotion.
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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.