ArticleApplied clinical informatics2023
Australasian Institute of Digital Health Summit 2022-Automated Social Media Surveillance for Detection of Vaccine Safety Signals: A Validation Study.
Article in Applied clinical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed, 6 citations in OpenAlex.
- Enhancing Relation Extraction for COVID-19 Vaccine Shot-Adverse Event Associations with Large Language Models.Research square · 2025Article
- Pharmacovigilance in the digital age: gaining insight from social media data.Experimental biology and medicine (Maywood, N.J.) · 2025Review
- The Value of Social Media Analysis for Adverse Events Detection and Pharmacovigilance: Scoping Review.JMIR public health and surveillance · 2024Article
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSocial media platforms have emerged as a valuable data source for public health research and surveillance. Monitoring of social media and user-generated data on the Web enables timely and inexpensive collection of information, overcoming time lag and cost of traditional health reporting systems.
objectivesThis article identifies personally experienced coronavirus disease 2019 (COVID-19) vaccine reactions expressed on Twitter and validate the findings against an established vaccine reactions reporting system.
methodsWe collected around 3 million tweets from 1.4 million users between February 1, 2021, to January 31, 2022, using COVID-19 vaccines and vaccine reactions keyword lists. We performed topic modeling on a sample of the data and applied a modified F1 scoring technique to identify a topic that best differentiated vaccine-related personal health mentions. We then manually annotated 4,000 of the records from this topic, which were used to train a transformer-based classifier to identify likely personally experienced vaccine reactions. Applying the trained classifier to the entire data set allowed us to select records we could use to quantify potential vaccine side effects. Adverse events following immunization (AEFI) referred to in these records were compared with those reported to the state of Victoria's spontaneous vaccine safety surveillance system, SAEFVIC (Surveillance of Adverse Events Following Vaccination In the Community).
resultsThe most frequently mentioned potential vaccine reactions generally aligned with SAEFVIC data. Notable exceptions were increased Twitter reporting of bleeding-related AEFI and allergic reactions, and more frequent SAEFVIC reporting of cardiac AEFI.
conclusionSocial media conversations are a potentially valuable supplementary data source for detecting vaccine adverse event mentions. Monitoring of online observations about new vaccine-related personal health experiences has the capacity to provide early warnings about emerging vaccine safety issues.
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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.