StanceXplore: Visualization for the Interactive Exploration of Stance in Social Media
Martins, Rafael, M. ; Simaki, Vasiliki LU ; Kucher, Kostiantyn ; Paradis, Carita LU
and Kerren, Andreas
(2017)
2nd Workshop on Visualization for the Digital Humanities- Abstract
- The use of interactive visualization techniques in Digital Humanities research can be a useful addition when traditional automated machine learning techniques face difficulties, as is often the case with the exploration of large volumes of dynamic—and in many cases, noisy and conflicting—textual data from social media. Recently, the field of stance analysis has been moving from a predominantly binary approach—either pro or con—to a multifaceted one, where each unit of text may be classified as one (or more) of multiple possible stance categories. This change adds more layers of complexity to an already hard problem, but also opens up new opportunities for obtaining richer and more relevant results from the analysis of stance-taking in... (More)
- The use of interactive visualization techniques in Digital Humanities research can be a useful addition when traditional automated machine learning techniques face difficulties, as is often the case with the exploration of large volumes of dynamic—and in many cases, noisy and conflicting—textual data from social media. Recently, the field of stance analysis has been moving from a predominantly binary approach—either pro or con—to a multifaceted one, where each unit of text may be classified as one (or more) of multiple possible stance categories. This change adds more layers of complexity to an already hard problem, but also opens up new opportunities for obtaining richer and more relevant results from the analysis of stance-taking in social media. In this paper we propose StanceXplore, a new visualization for the interactive exploration of stance in social media. Our goal is to offer DH researchers the chance to explore stance-classified text corpora from different perspectives at the same time, using coordinated multiple views including user-defined topics, content similarity and dissimilarity, and geographical and temporal distribution. As a case study, we explore the activity of Twitter users in Sweden, analyzing their behavior in terms of topics discussed and the stances taken. Each textual unit (tweet) is labeled with one of eleven stance categories from a cognitive-functional stance framework based on recent work. We illustrate how StanceXplore can be used effectively to investigate multidimensional patterns and trends in stance-taking related to cultural events, their geographical distribution, and the confidence of the stance classifier. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/044aca97-ecdb-4769-81a3-f786492dc75b
- author
- Martins, Rafael, M.
; Simaki, Vasiliki
LU
; Kucher, Kostiantyn
; Paradis, Carita
LU
and Kerren, Andreas
- organization
- publishing date
- 2017
- type
- Contribution to conference
- publication status
- published
- subject
- keywords
- text mining, digital humanities, social media, machine learning
- pages
- 5 pages
- conference name
- 2nd Workshop on Visualization for the Digital Humanities<br/>
- conference location
- Phoenix, United States
- conference dates
- 2017-10-02 - 2017-10-02
- project
- StaViCTA - Advances in the description and explanation of stance in discourse using visual and computational text analytics
- language
- English
- LU publication?
- yes
- id
- 044aca97-ecdb-4769-81a3-f786492dc75b
- date added to LUP
- 2018-03-29 10:03:19
- date last changed
- 2019-11-26 03:12:09
@misc{044aca97-ecdb-4769-81a3-f786492dc75b,
abstract = {{The use of interactive visualization techniques in Digital Humanities research can be a useful addition when traditional automated machine learning techniques face difficulties, as is often the case with the exploration of large volumes of dynamic—and in many cases, noisy and conflicting—textual data from social media. Recently, the field of stance analysis has been moving from a predominantly binary approach—either pro or con—to a multifaceted one, where each unit of text may be classified as one (or more) of multiple possible stance categories. This change adds more layers of complexity to an already hard problem, but also opens up new opportunities for obtaining richer and more relevant results from the analysis of stance-taking in social media. In this paper we propose StanceXplore, a new visualization for the interactive exploration of stance in social media. Our goal is to offer DH researchers the chance to explore stance-classified text corpora from different perspectives at the same time, using coordinated multiple views including user-defined topics, content similarity and dissimilarity, and geographical and temporal distribution. As a case study, we explore the activity of Twitter users in Sweden, analyzing their behavior in terms of topics discussed and the stances taken. Each textual unit (tweet) is labeled with one of eleven stance categories from a cognitive-functional stance framework based on recent work. We illustrate how StanceXplore can be used effectively to investigate multidimensional patterns and trends in stance-taking related to cultural events, their geographical distribution, and the confidence of the stance classifier.}},
author = {{Martins, Rafael, M. and Simaki, Vasiliki and Kucher, Kostiantyn and Paradis, Carita and Kerren, Andreas}},
keywords = {{text mining; digital humanities; social media; machine learning}},
language = {{eng}},
title = {{StanceXplore: Visualization for the Interactive Exploration of Stance in Social Media}},
url = {{https://lup.lub.lu.se/search/files/40622801/martins_vis4dh17.pdf}},
year = {{2017}},
}