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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.
Uren et al. (Mon,) studied this question.