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

Research output: Contribution to journalArticle

Abstract

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.

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Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • 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
Original languageEnglish
Pages (from-to)93-116
JournalInformation Visualization
Volume15
Issue number2
Publication statusPublished - 2016
Publication categoryResearch
Peer-reviewedYes

Bibliographic note

Published online before print March 26, 2015

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