Detection of Stance-Related Characteristics in Social Media Text

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

Standard

Detection of Stance-Related Characteristics in Social Media Text. / Simaki, Vasiliki; Simakis, Panagiotis; Paradis, Carita; Kerren, Andreas.

SETN '18 Proceedings of the 10th Hellenic Conference on Artificial Intelligence . New York : Association for Computing Machinery (ACM), 2018.

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

Harvard

Simaki, V, Simakis, P, Paradis, C & Kerren, A 2018, Detection of Stance-Related Characteristics in Social Media Text. in SETN '18 Proceedings of the 10th Hellenic Conference on Artificial Intelligence . Association for Computing Machinery (ACM), New York, The 10th Hellenic Conference on Artificial Intelligence , Patras, Greece, 2018/07/09. https://doi.org/10.1145/3200947.3201017

APA

Simaki, V., Simakis, P., Paradis, C., & Kerren, A. (2018). Detection of Stance-Related Characteristics in Social Media Text. In SETN '18 Proceedings of the 10th Hellenic Conference on Artificial Intelligence Association for Computing Machinery (ACM). https://doi.org/10.1145/3200947.3201017

CBE

Simaki V, Simakis P, Paradis C, Kerren A. 2018. Detection of Stance-Related Characteristics in Social Media Text. In SETN '18 Proceedings of the 10th Hellenic Conference on Artificial Intelligence . New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/3200947.3201017

MLA

Simaki, Vasiliki et al. "Detection of Stance-Related Characteristics in Social Media Text". SETN '18 Proceedings of the 10th Hellenic Conference on Artificial Intelligence . New York: Association for Computing Machinery (ACM). 2018. https://doi.org/10.1145/3200947.3201017

Vancouver

Simaki V, Simakis P, Paradis C, Kerren A. Detection of Stance-Related Characteristics in Social Media Text. In SETN '18 Proceedings of the 10th Hellenic Conference on Artificial Intelligence . New York: Association for Computing Machinery (ACM). 2018 https://doi.org/10.1145/3200947.3201017

Author

Simaki, Vasiliki ; Simakis, Panagiotis ; Paradis, Carita ; Kerren, Andreas. / Detection of Stance-Related Characteristics in Social Media Text. SETN '18 Proceedings of the 10th Hellenic Conference on Artificial Intelligence . New York : Association for Computing Machinery (ACM), 2018.

RIS

TY - GEN

T1 - Detection of Stance-Related Characteristics in Social Media Text

AU - Simaki, Vasiliki

AU - Simakis, Panagiotis

AU - Paradis, Carita

AU - Kerren, Andreas

N1 - Conference code: 10

PY - 2018/7/15

Y1 - 2018/7/15

N2 - 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.

AB - 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.

KW - stance-taking

KW - text

KW - clustering

KW - feature extraction

KW - social media

U2 - 10.1145/3200947.3201017

DO - 10.1145/3200947.3201017

M3 - Paper in conference proceeding

SN - 978-1-4503-6433-1

BT - SETN '18 Proceedings of the 10th Hellenic Conference on Artificial Intelligence

PB - Association for Computing Machinery (ACM)

CY - New York

T2 - The 10th Hellenic Conference on Artificial Intelligence

Y2 - 9 July 2018 through 15 July 2018

ER -