Visual analysis of stance markers in online social media

Kostiantyn Kucher; Andreas Kerren; Carita Paradis; Magnus Sahlgren

doi:10.1109/VAST.2014.7042519

Visual analysis of stance markers in online social media

Kostiantyn Kucher, Andreas Kerren, Carita Paradis, Magnus Sahlgren

English Studies

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

Abstract

Stance in human communication is a linguistic concept relating to expressions of subjectivity such as the speakers' attitudes and emotions. Taking stance is crucial for the social construction of meaning and can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the results of computational linguistics techniques. Both aspects are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data and corresponding time-series that can be used to investigate stance phenomena and to refine the so-called stance markers collection.

Original language	English
Title of host publication	2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014
Subtitle of host publication	Proceedings. Paris, France, 9-14 October 2014
Place of Publication	Piscataway, NJ
Publisher	IEEE - Institute of Electrical and Electronics Engineers Inc.
Pages	259-260
Number of pages	2
ISBN (Print)	9781479962273
DOIs	https://doi.org/10.1109/VAST.2014.7042519
Publication status	Published - 2015 Feb 13
Event	2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - The Marriott Rive-Gauche, Paris, France Duration: 2014 Nov 9 → 2014 Nov 14

Conference

Conference	2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014
Country/Territory	France
City	Paris
Period	2014/11/09 → 2014/11/14

Subject classification (UKÄ)

Languages and Literature
Media and Communications

Free keywords

interaction
NLP
sentiment analysis
stance analysis
text analytics
text visualization
time-series
Visualization

Access to Document

10.1109/VAST.2014.7042519

Cite this

Kucher, K., Kerren, A., Paradis, C., & Sahlgren, M. (2015). Visual analysis of stance markers in online social media. In 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014: Proceedings. Paris, France, 9-14 October 2014 (pp. 259-260). Article 7042519 IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VAST.2014.7042519

@inproceedings{9b0034427d544cdd83512746a27726d9,

title = "Visual analysis of stance markers in online social media",

abstract = "Stance in human communication is a linguistic concept relating to expressions of subjectivity such as the speakers' attitudes and emotions. Taking stance is crucial for the social construction of meaning and can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the results of computational linguistics techniques. Both aspects are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data and corresponding time-series that can be used to investigate stance phenomena and to refine the so-called stance markers collection.",

keywords = "interaction, NLP, sentiment analysis, stance analysis, text analytics, text visualization, time-series, Visualization",

author = "Kostiantyn Kucher and Andreas Kerren and Carita Paradis and Magnus Sahlgren",

year = "2015",

month = feb,

day = "13",

doi = "10.1109/VAST.2014.7042519",

language = "English",

isbn = "9781479962273",

pages = "259--260",

booktitle = "2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 ; Conference date: 09-11-2014 Through 14-11-2014",

}

Kucher, K, Kerren, A, Paradis, C & Sahlgren, M 2015, Visual analysis of stance markers in online social media. in 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014: Proceedings. Paris, France, 9-14 October 2014., 7042519, IEEE - Institute of Electrical and Electronics Engineers Inc., Piscataway, NJ, pp. 259-260, 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014, Paris, France, 2014/11/09. https://doi.org/10.1109/VAST.2014.7042519

Visual analysis of stance markers in online social media. / Kucher, Kostiantyn; Kerren, Andreas; Paradis, Carita et al.
2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014: Proceedings. Paris, France, 9-14 October 2014. Piscataway, NJ: IEEE - Institute of Electrical and Electronics Engineers Inc., 2015. p. 259-260 7042519.

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

TY - GEN

T1 - Visual analysis of stance markers in online social media

AU - Kucher, Kostiantyn

AU - Kerren, Andreas

AU - Paradis, Carita

AU - Sahlgren, Magnus

PY - 2015/2/13

Y1 - 2015/2/13

N2 - Stance in human communication is a linguistic concept relating to expressions of subjectivity such as the speakers' attitudes and emotions. Taking stance is crucial for the social construction of meaning and can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the results of computational linguistics techniques. Both aspects are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data and corresponding time-series that can be used to investigate stance phenomena and to refine the so-called stance markers collection.

AB - Stance in human communication is a linguistic concept relating to expressions of subjectivity such as the speakers' attitudes and emotions. Taking stance is crucial for the social construction of meaning and can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the results of computational linguistics techniques. Both aspects are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data and corresponding time-series that can be used to investigate stance phenomena and to refine the so-called stance markers collection.

KW - interaction

KW - NLP

KW - sentiment analysis

KW - stance analysis

KW - text analytics

KW - text visualization

KW - time-series

KW - Visualization

UR - http://www.scopus.com/inward/record.url?scp=84929460615&partnerID=8YFLogxK

U2 - 10.1109/VAST.2014.7042519

DO - 10.1109/VAST.2014.7042519

M3 - Paper in conference proceeding

SN - 9781479962273

SP - 259

EP - 260

BT - 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

CY - Piscataway, NJ

T2 - 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014

Y2 - 9 November 2014 through 14 November 2014

ER -

Visual analysis of stance markers in online social media

Abstract

Conference

Subject classification (UKÄ)

Free keywords

Access to Document

Other files and links

Fingerprint

Cite this