The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Visual analysis of online social media to open up the investigation of stance phenomena

På svenska

Author

Summary, in English

Online social media are a perfect text source for stance analysis. Stance in human communication is concerned with speaker attitudes, beliefs, feelings and opinions. Expressions of stance are associated with the speakers' view of what they are talking about and what is up for discussion and negotiation in the intersubjective exchange. Taking stance is thus crucial for the social construction of meaning. Increased knowledge of stance 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 processed data based on computational linguistics techniques. Both original texts and derived data are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data that can be used to open up the investigation of stance phenomena. Our approach complements traditional linguistic analysis techniques and is based on the analysis of utterances associated with two stance categories: sentiment and certainty. Our contributions include (1) the description of a novel web-based solution for analyzing the use and patterns of stance meanings and expressions in human communication over time; and (2) specialized techniques used for visualizing analysis provenance and corpus overview/navigation. We demonstrate our approach by means of text media on a highly controversial scandal with regard to expressions of anger and provide an expert review from linguists who have been using our tool.

Department/s

Publishing year

2016

Language

English

Pages

93-116

Publication/Series

Information Visualization

Volume

15

Issue

2

Document type

Journal article

Publisher

SAGE Publications

Topic

  • Languages and Literature
  • Computer and Information Science

Keywords

  • text and document data
  • online social media
  • visual linguistics
  • text analytics
  • sentiment analysis
  • stance analysis
  • time-series
  • interaction
  • text visualization
  • visualization
  • Visual analytics

Status

Published

Project

  • StaViCTA - Advances in the description and explanation of stance in discourse using visual and computational text analytics

Research group

  • Language, Cognition and Discourse@Lund (LCD@L)

ISBN/ISSN/Other

  • ISSN: 1473-8724