Visual analysis of stance markers in online social media
Kucher, Kostiantyn ; Kerren, Andreas ; Paradis, Carita LU
and Sahlgren, Magnus
(2014)
IEEE Visual Analytics Science and Technology (VAST '14),
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4586851
- author
- Kucher, Kostiantyn
; Kerren, Andreas
; Paradis, Carita
LU
and Sahlgren, Magnus
- organization
- publishing date
- 2014
- type
- Contribution to conference
- publication status
- published
- subject
- keywords
- visualization, text visualization, interaction, time-series, stance analsyis, sentiment analysis, NLP, text analystics
- conference name
- IEEE Visual Analytics Science and Technology (VAST '14),
- conference dates
- 2014-09-14
- language
- English
- LU publication?
- yes
- id
- 5ff5701c-ae38-4ada-b492-f518069c992f (old id 4586851)
- date added to LUP
- 2016-04-04 14:08:52
- date last changed
- 2019-03-08 02:28:54
@misc{5ff5701c-ae38-4ada-b492-f518069c992f,
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.}},
author = {{Kucher, Kostiantyn and Kerren, Andreas and Paradis, Carita and Sahlgren, Magnus}},
keywords = {{visualization; text visualization; interaction; time-series; stance analsyis; sentiment analysis; NLP; text analystics}},
language = {{eng}},
title = {{Visual analysis of stance markers in online social media}},
year = {{2014}},
}