Detection of Stance-Related Characteristics in Social Media Text

Vasiliki Simaki, Panagiotis Simakis, Carita Paradis, Andreas Kerren

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceedingPeer review

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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.
Titel på värdpublikationSETN '18 Proceedings of the 10th Hellenic Conference on Artificial Intelligence
UtgivningsortNew York
FörlagAssociation for Computing Machinery (ACM)
Antal sidor7
ISBN (tryckt)978-1-4503-6433-1
StatusPublished - 2018 juli 15
EvenemangThe 10th Hellenic Conference on Artificial Intelligence - University of Patras, Patras, Grekland
Varaktighet: 2018 juli 92018 juli 15
Konferensnummer: 10


KonferensThe 10th Hellenic Conference on Artificial Intelligence
Förkortad titelSETN '18

Ämnesklassifikation (UKÄ)

  • Jämförande språkvetenskap och lingvistik


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