Visual Twitter Analytics (Vista)

Orland Hoeber (Department of Computer Science, University of Regina, Regina, Canada)
Larena Hoeber (Faculty of Kinesiology and Health Studies, University of Regina, University of Regina, Regina, Canada)
Maha El Meseery (Department of Computer Science, University of Regina, Regina, Canada)
Kenneth Odoh (Department of Computer Science, University of Regina, Regina, Canada)
Radhika Gopi (Department of Computer Science, University of Regina, Regina, Canada)

Online Information Review

ISSN: 1468-4527

Publication date: 8 February 2016

Abstract

Purpose

Due to the size and velocity at which user generated content is created on social media services such as Twitter, analysts are often limited by the need to pre-determine the specific topics and themes they wish to follow. Visual analytics software may be used to support the interactive discovery of emergent themes. The paper aims to discuss these issues.

Design/methodology/approach

Tweets collected from the live Twitter stream matching a user’s query are stored in a database, and classified based on their sentiment. The temporally changing sentiment is visualized, along with sparklines showing the distribution of the top terms, hashtags, user mentions, and authors in each of the positive, neutral, and negative classes. Interactive tools are provided to support sub-querying and the examination of emergent themes.

Findings

A case study of using Vista to analyze sport fan engagement within a mega-sport event (2013 Le Tour de France) is provided. The authors illustrate how emergent themes can be identified and isolated from the large collection of data, without the need to identify these a priori.

Originality/value

Vista provides mechanisms that support the interactive exploration among Twitter data. By combining automatic data processing and machine learning methods with interactive visualization software, researchers are relieved of tedious data processing tasks, and can focus on the analysis of high-level features of the data. In particular, patterns of Twitter use can be identified, emergent themes can be isolated, and purposeful samples of the data can be selected by the researcher for further analysis.

Keywords

Citation

Hoeber, O., Hoeber, L., El Meseery, M., Odoh, K. and Gopi, R. (2016), "Visual Twitter Analytics (Vista)", Online Information Review, Vol. 40 No. 1, pp. 25-41. https://doi.org/10.1108/OIR-02-2015-0067

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Publisher

:

Emerald Group Publishing Limited

Copyright © 2016, Emerald Group Publishing Limited

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