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Vaccine Hesitancy in Discussion Forums: Computer-Assisted Argument Mining with Topic Models
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Potsdam University, Germany. (ISOVIS)ORCID iD: 0000-0001-6164-7762
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. (ISOVIS)ORCID iD: 0000-0002-0519-2537
Potsdam University, Germany.
2018 (English)In: Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth / [ed] Adrien Ugon, Daniel Karlsson, Gunnar O. Klein, and Anne Moen, IOS Press, 2018, p. 366-370Conference paper, Published paper (Refereed)
Abstract [en]

Arguments used when vaccination is debated on Internet discussion forums might give us valuable insights into reasons behind vaccine hesitancy. In this study, we applied automatic topic modelling on a collection of 943 discussion posts in which vaccine was debated, and six distinct discussion topics were detected by the algorithm. When manually coding the posts ranked as most typical for these six topics, a set of semantically coherent arguments were identified for each extracted topic. This indicates that topic modelling is a useful method for automatically identifying vaccine-related discussion topics and for identifying debate posts where these topics are discussed. This functionality could facilitate manual coding of salient arguments, and thereby form an important component in a system for computer-assisted coding of vaccine-related discussions. 

Place, publisher, year, edition, pages
IOS Press, 2018. p. 366-370
Series
Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365 ; 247
Keywords [en]
vaccine hesitancy, topic modelling, argument mining
National Category
Language Technology (Computational Linguistics)
Research subject
Computer Science, Information and software visualization
Identifiers
URN: urn:nbn:se:lnu:diva-70919DOI: 10.3233/978-1-61499-852-5-366ISBN: 978-1-61499-851-8 (print)ISBN: 978-1-61499-852-5 (electronic)OAI: oai:DiVA.org:lnu-70919DiVA, id: diva2:1182970
Conference
29th Medical Informatics Europe Conference (MIE '18), April 24-26, 2018, Gothenburg, Sweden
Projects
StaViCTA
Funder
Swedish Research Council, 2016-06681Swedish Research Council, 2012-5659Available from: 2018-02-15 Created: 2018-02-15 Last updated: 2018-04-24

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Skeppstedt, MariaKerren, Andreas

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
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