lnu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
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
  • html
  • text
  • asciidoc
  • rtf
Detection of Stance-Related Characteristics in Social Media Text
Lancaster University, UK ; Lund University.ORCID iD: 0000-0002-8998-3618
XPLAIN, Greece.
Lund University.ORCID iD: 0000-0002-7240-9003
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ISOVIS)ORCID iD: 0000-0002-0519-2537
2018 (English)In: Proceedings of the 10th Hellenic Conference on Artificial Intelligence (SETN '18), ACM Publications, 2018, article id 38Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we present a study for the identification of stance-related features in text data from social media. Based on our previous work on stance and our findings on stance patterns, we detected stance-related characteristics in a data set from Twitter and Facebook. We extracted various corpus-, quantitative- and computational-based features that proved to be significant for six stance categories (contrariety, hypotheticality, necessity, prediction, source of knowledge, and uncertainty), and we tested them in our data set. The results of a preliminary clustering method are presented and discussed as a starting point for future contributions in the field. The results of our experiments showed a strong correlation between different characteristics and stance constructions, which can lead us to a methodology for automatic stance annotation of these data.

Place, publisher, year, edition, pages
ACM Publications, 2018. article id 38
Keywords [en]
stance-taking, text clustering, feature extraction, social media text
National Category
Language Technology (Computational Linguistics)
Research subject
Computer Science, Information and software visualization
Identifiers
URN: urn:nbn:se:lnu:diva-71735DOI: 10.1145/3200947.3201017ISBN: 978-1-4503-6433-1 (electronic)OAI: oai:DiVA.org:lnu-71735DiVA, id: diva2:1192314
Conference
The 10th Hellenic Conference on Artificial Intelligence (SETN '18), 9-15 July 2018, Patras, Greece
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659Available from: 2018-03-22 Created: 2018-03-22 Last updated: 2018-09-10Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Kerren, Andreas

Search in DiVA

By author/editor
Simaki, VasilikiParadis, CaritaKerren, Andreas
By organisation
Department of computer science and media technology (CM)
Language Technology (Computational Linguistics)

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 60 hits
CiteExportLink to record
Permanent link

Direct link
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
  • html
  • text
  • asciidoc
  • rtf