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Detection of Stance-Related Characteristics in Social Media Text

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Detection of Stance-Related Characteristics in Social Media Text. / Simaki, Vasiliki; Simakis, Panagiotis; Paradis, Carita et al.
SETN '18 Proceedings of the 10th Hellenic Conference on Artificial Intelligence: 10th Hellenic Conference on Artificial Intelligence. New York: Association for Computing Machinery, Inc, 2018. 38.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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: 10th Hellenic Conference on Artificial Intelligence., 38, Association for Computing Machinery, Inc, New York. 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: 10th Hellenic Conference on Artificial Intelligence Article 38 Association for Computing Machinery, Inc. 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: 10th Hellenic Conference on Artificial Intelligence. New York: Association for Computing Machinery, Inc. 2018. 38 doi: 10.1145/3200947.3201017

Author

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

Bibtex

@inproceedings{757434be6a3e44879c8ab70055ef5f19,
title = "Detection of Stance-Related Characteristics in Social Media Text",
abstract = "In this paper, we present a study for the identification of stance-related features in text data from social media. Based on our previous work on stance and our findings on stance patterns, wedetected 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 methodare 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 = "Vasiliki Simaki and Panagiotis Simakis and Carita Paradis and Andreas Kerren",
year = "2018",
month = jul,
day = "9",
doi = "10.1145/3200947.3201017",
language = "English",
isbn = "9781450364331",
booktitle = "SETN '18 Proceedings of the 10th Hellenic Conference on Artificial Intelligence",
publisher = "Association for Computing Machinery, Inc",

}

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

PY - 2018/7/9

Y1 - 2018/7/9

N2 - In this paper, we present a study for the identification of stance-related features in text data from social media. Based on our previous work on stance and our findings on stance patterns, wedetected 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 methodare 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 stance-related features in text data from social media. Based on our previous work on stance and our findings on stance patterns, wedetected 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 methodare 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.

U2 - 10.1145/3200947.3201017

DO - 10.1145/3200947.3201017

M3 - Conference contribution/Paper

SN - 9781450364331

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

PB - Association for Computing Machinery, Inc

CY - New York

ER -