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StanceXplore: Visualization for the Interactive Exploration of Stance in Social Media
Linnaeus University, Faculty of Technology, Department of Computer Science. (ISOVIS)ORCID iD: 0000-0002-2901-935X
Linnaeus University, Faculty of Technology, Department of Computer Science. Lund University. (ISOVIS)ORCID iD: 0000-0002-8998-3618
Linnaeus University, Faculty of Technology, Department of Computer Science. (ISOVIS)ORCID iD: 0000-0002-1907-7820
Lund University.ORCID iD: 0000-0002-7240-9003
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2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

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 stancetaking 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. 

Place, publisher, year, edition, pages
2017.
Keyword [en]
Stance Visualization, Sentiment Analysis, Digital Humanities, Visual Analytics, Social Media Text
National Category
Human Computer Interaction Computer Science Language Technology (Computational Linguistics)
Research subject
Computer Science, Information and software visualization
Identifiers
URN: urn:nbn:se:lnu:diva-67320OAI: oai:DiVA.org:lnu-67320DiVA: diva2:1134669
Conference
2nd Workshop on Visualization for the Digital Humanities (VIS4DH '17), October 2017, Phoenix, Arizona, USA
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Note

TO BE PUBLISHED!

Available from: 2017-08-21 Created: 2017-08-21 Last updated: 2017-09-15

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Martins, Rafael MessiasSimaki, VasilikiKucher, KostiantynParadis, CaritaKerren, Andreas
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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Language
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Output format
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