Detection of Stance-Related Characteristics in Social Media Text

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

Bibtex

@inproceedings{e8c1cdaa6c8c4865b0b0b165ab862a96,
title = "Detection of Stance-Related Characteristics in Social Media Text",
abstract = "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.",
keywords = "stance-taking, text, clustering, feature extraction, social media",
author = "Vasiliki Simaki and Panagiotis Simakis and Carita Paradis and Andreas Kerren",
year = "2018",
month = jul,
day = "15",
doi = "10.1145/3200947.3201017",
language = "English",
isbn = "978-1-4503-6433-1",
booktitle = "SETN '18 Proceedings of the 10th Hellenic Conference on Artificial Intelligence",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",
note = "The 10th Hellenic Conference on Artificial Intelligence , SETN '18 ; Conference date: 09-07-2018 Through 15-07-2018",
url = "http://setn2018.upatras.gr/",

}