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Detection of Stance-Related Characteristics in Social Media Text

Simaki, Vasiliki LU ; Simakis, Panagiotis ; Paradis, Carita LU and Kerren, Andreas (2018) The 10th Hellenic Conference on Artificial Intelligence
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
stance-taking, text, clustering, feature extraction, social media
host publication
SETN '18 Proceedings of the 10th Hellenic Conference on Artificial Intelligence
pages
7 pages
publisher
Association for Computing Machinery (ACM)
conference name
The 10th Hellenic Conference on Artificial Intelligence
conference location
Patras, Greece
conference dates
2018-07-09 - 2018-07-15
external identifiers
  • scopus:85052024070
ISBN
978-1-4503-6433-1
DOI
10.1145/3200947.3201017
project
StaViCTA - Advances in the description and explanation of stance in discourse using visual and computational text analytics
language
English
LU publication?
yes
id
e8c1cdaa-6c8c-4865-b0b0-b165ab862a96
date added to LUP
2018-04-27 09:41:49
date last changed
2020-01-13 00:40:29
@inproceedings{e8c1cdaa-6c8c-4865-b0b0-b165ab862a96,
  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.},
  author       = {Simaki, Vasiliki and Simakis, Panagiotis and Paradis, Carita and Kerren, Andreas},
  booktitle    = {SETN '18 Proceedings of the 10th Hellenic Conference on Artificial Intelligence },
  isbn         = {978-1-4503-6433-1},
  language     = {eng},
  month        = {07},
  publisher    = {Association for Computing Machinery (ACM)},
  title        = {Detection of Stance-Related Characteristics in Social Media Text},
  url          = {https://lup.lub.lu.se/search/ws/files/42491998/Simaki_Paradi_Kerren_Patras_sample_sigconf.pdf},
  doi          = {10.1145/3200947.3201017},
  year         = {2018},
}