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Representing vaccine misinformation using ontologies.

Muhammad Amith1, Cui Tao2

  • 1School of Biomedical Informatics, The University of Texas Health Science Center, 7000 Fannin Street, Suite 600, Houston, TX, USA.

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|September 2, 2018
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Summary
This summary is machine-generated.

Researchers developed the Vaccine Misinformation Ontology (VAXMO) to formally structure knowledge about vaccine misinformation. This tool aids in detecting and analyzing anti-vaccine content, combating vaccine hesitancy.

Keywords:
MicroattributionMisinformationNatural language processingOntologySemantic similaritySemantic webVaccine

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Area of Science:

  • Informatics
  • Ontology Engineering
  • Public Health

Background:

  • Vaccine misinformation is a significant driver of vaccine hesitancy.
  • Understanding and combating misinformation requires robust analytical tools.
  • Existing methods lack formal structures for analyzing vaccine-related falsehoods.

Purpose of the Study:

  • To design and develop a formal ontology for describing vaccine misinformation.
  • To create a structured knowledge base for analyzing anti-vaccine content.
  • To support the development of informatics solutions for vaccine hesitancy.

Main Methods:

  • Developed the Vaccine Misinformation Ontology (VAXMO).
  • Extended the existing Misinformation Ontology.
  • Linked VAXMO to the Resource Description Framework (RDF) model for false assertions.

Main Results:

  • Preliminary semiotic evaluation indicated adequate ontology quality.
  • Demonstrated proposed use cases for detecting and understanding anti-vaccine information.
  • VAXMO provides a formal structure for vaccine misinformation knowledge.

Conclusions:

  • VAXMO can support tools for vaccine misinformation detection and analysis.
  • Formalizing knowledge enables machines and software to better understand vaccine misinformation.
  • The ontology is a foundational step towards informatics solutions for vaccine hesitancy.