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Visual analysis of stance markers in online social media

Kucher, Kostiantyn; Kerren, Andreas; Paradis, Carita LU and Sahlgren, Magnus (2015) 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 In 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 p.259-260
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... (More)

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.

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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
interaction, NLP, sentiment analysis, stance analysis, text analytics, text visualization, time-series, Visualization
in
2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014
pages
2 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014
external identifiers
  • Scopus:84929460615
ISBN
9781479962273
DOI
10.1109/VAST.2014.7042519
language
English
LU publication?
yes
id
9b003442-7d54-4cdd-8351-2746a27726d9
date added to LUP
2016-05-17 10:48:37
date last changed
2016-10-11 08:58:18
@misc{9b003442-7d54-4cdd-8351-2746a27726d9,
  abstract     = {<p>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.</p>},
  author       = {Kucher, Kostiantyn and Kerren, Andreas and Paradis, Carita and Sahlgren, Magnus},
  isbn         = {9781479962273},
  keyword      = {interaction,NLP,sentiment analysis,stance analysis,text analytics,text visualization,time-series,Visualization},
  language     = {eng},
  month        = {02},
  pages        = {259--260},
  publisher    = {ARRAY(0xab1a9a0)},
  series       = {2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014},
  title        = {Visual analysis of stance markers in online social media},
  url          = {http://dx.doi.org/10.1109/VAST.2014.7042519},
  year         = {2015},
}