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Automatic detection of stance towards vaccination in online discussion forums
Linnaeus University, Faculty of Technology, Department of Computer Science. University of Potsdam, Germany. (ISOVIS)ORCID iD: 0000-0001-6164-7762
Linnaeus University, Faculty of Technology, Department of Computer Science. (ISOVIS)ORCID iD: 0000-0002-0519-2537
University of Potsdam, Germany.
2017 (English)In: Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017) / [ed] Jitendra Jonnagaddala, Hong-Jie Dai, and Yung-Chun Chang, Association for Computational Linguistics, 2017, p. 1-8Conference paper, Published paper (Refereed)
Abstract [en]

A classifier for automatic detection of stance towards vaccination in online forums was trained and evaluated. Debate posts from six discussion threads on the British parental website Mumsnet were manually annotated for stance against or for vaccination, or as undecided. A support vector machine, trained to detect the three classes, achieved a macro F-score of 0.44, while a macro F-score of 0.62 was obtained by the same type of classifier on the binary classification task of distinguishing stance against vaccination from stance for vaccination. These results show that vaccine stance detection in online forums is a difficult task, at least for the type of model investigated and for the relatively small training corpus that was used. Fu- ture work will therefore include an expansion of the training data and an evaluation of other types of classifiers and features. 

Place, publisher, year, edition, pages
Association for Computational Linguistics, 2017. p. 1-8
Keywords [en]
stance, online forums, classifier, support vector machine, vaccination
National Category
Language Technology (Computational Linguistics)
Research subject
Computer Science, Information and software visualization
Identifiers
URN: urn:nbn:se:lnu:diva-68982ISBN: 978-1-948087-07-0 (electronic)OAI: oai:DiVA.org:lnu-68982DiVA, id: diva2:1160003
Conference
1st International Workshop on Digital Disease Detection using Social Media (DDDSM), Taipei, Taiwan, 27 November, 2017
Projects
StaViCTANavigating in streams of opinions
Funder
Swedish Research Council, 2016-06681Swedish Research Council, 2012-5659Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2018-02-09Bibliographically approved

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Skeppstedt, MariaKerren, Andreas

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CiteExportLink to record
Permanent link

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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
  • html
  • text
  • asciidoc
  • rtf