Detection of Stance-Related Characteristics in Social Media Text
(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:
https://lup.lub.lu.se/record/e8c1cdaa-6c8c-4865-b0b0-b165ab862a96
- author
- Simaki, Vasiliki LU ; Simakis, Panagiotis ; Paradis, Carita LU and Kerren, Andreas
- organization
- publishing date
- 2018-07-15
- 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
- 2022-01-31 03:18:01
@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}}, keywords = {{stance-taking; text; clustering; feature extraction; social media}}, 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/files/42491998/Simaki_Paradi_Kerren_Patras_sample_sigconf.pdf}}, doi = {{10.1145/3200947.3201017}}, year = {{2018}}, }