Detection of Stance-Related Characteristics in Social Media Text

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding


In this paper, we present a study for the identification of stancerelated 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.


External organisations
  • Linnaeus University
  • Lancaster University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • General Language Studies and Linguistics


  • stance-taking, text, clustering, feature extraction, social media
Original languageEnglish
Title of host publicationSETN '18 Proceedings of the 10th Hellenic Conference on Artificial Intelligence
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages7
ISBN (Print)978-1-4503-6433-1
Publication statusPublished - 2018 Jul 15
Publication categoryResearch
EventThe 10th Hellenic Conference on Artificial Intelligence - University of Patras, Patras, Greece
Duration: 2018 Jul 92018 Jul 15
Conference number: 10


ConferenceThe 10th Hellenic Conference on Artificial Intelligence
Abbreviated titleSETN '18
Internet address

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