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Public science communication on Twitter: a visual analytic approach

Public science communication on Twitter: a visual analytic approach Purpose– The purpose of this paper is to assess high-dimensional visualisation, combined with pattern matching, as an approach to observing dynamic changes in the ways people tweet about science topics. Design/methodology/approach– The high-dimensional visualisation approach was applied to three science topics to test its effectiveness for longitudinal analysis of message framing on Twitter over two disjoint periods in time. The paper uses coding frames to drive categorisation and visual analytics of tweets discussing the science topics. Findings– The findings point to the potential of this mixed methods approach, as it allows sufficiently high sensitivity to recognise and support the analysis of non-trending as well as trending topics on Twitter. Research limitations/implications– Three topics are studied, these illustrate a range of frames, but results may not be representative of all science topics. Social implications– Funding bodies increasingly encourage scientists to participate in public engagement. As social media provides an avenue actively utilised for public communication, understanding the nature of the dialog on this medium is important for the scientific community and the public at large. Originality/value– This study differs from standard approaches to the analysis of micropost data, which tend to focus on large-scale data sets. It provides evidence that this approach enables practical and effective analysis of the content of midsize to large collections of microposts. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Aslib Journal of Information Management Emerald Publishing

Public science communication on Twitter: a visual analytic approach

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
2050-3806
DOI
10.1108/AJIM-10-2014-0137
Publisher site
See Article on Publisher Site

Abstract

Purpose– The purpose of this paper is to assess high-dimensional visualisation, combined with pattern matching, as an approach to observing dynamic changes in the ways people tweet about science topics. Design/methodology/approach– The high-dimensional visualisation approach was applied to three science topics to test its effectiveness for longitudinal analysis of message framing on Twitter over two disjoint periods in time. The paper uses coding frames to drive categorisation and visual analytics of tweets discussing the science topics. Findings– The findings point to the potential of this mixed methods approach, as it allows sufficiently high sensitivity to recognise and support the analysis of non-trending as well as trending topics on Twitter. Research limitations/implications– Three topics are studied, these illustrate a range of frames, but results may not be representative of all science topics. Social implications– Funding bodies increasingly encourage scientists to participate in public engagement. As social media provides an avenue actively utilised for public communication, understanding the nature of the dialog on this medium is important for the scientific community and the public at large. Originality/value– This study differs from standard approaches to the analysis of micropost data, which tend to focus on large-scale data sets. It provides evidence that this approach enables practical and effective analysis of the content of midsize to large collections of microposts.

Journal

Aslib Journal of Information ManagementEmerald Publishing

Published: May 18, 2015

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