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Crowd Mining Applied to Preservation of Digital Cultural Heritage

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Museum Experience Design

Part of the book series: Springer Series on Cultural Computing ((SSCC))

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Abstract

Accessible systems, in digital heritage as elsewhere, should ‘speak the user’s language’. However, over long time periods, this may change significantly, and the system must still keep track of it. Conceptualising and tracking change in a population may be achieved using a functional and computable model based on representative datasets. Such a model must encompass relevant characteristics in that population and support predefined functionality, such as the ability to track current trends in language use. Individual published viewpoints on any given platform may be observed in aggregate by means of a large-scale text mining approach. We have made use of social media platforms such as Twitter and Tumblr to collect statistical information about anonymous users’ perspectives on cultural heritage items and institutions. Through longitudinal studies, it is possible to identify indicators pointing to an evolution of discourse surrounding cultural heritage items, and provide an estimate of trends relating to represented items and creators. We describe a functional approach to building useful models of shift in contemporary language use, using data collection across social networks. This approach is informed by existing theoretical approaches to modelling of semantic change. As a case study, we present a means by which such ongoing user modelling processes drawing on contemporary resources can support ‘just-in-time’ pre-emptive review of material to be presented to the public. We also show that this approach can feed into enhancement of the data retrieval processes.

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Notes

  1. 1.

    Analysis of the social collection website Pinterest (Mull and Lee 2014) shows that users of the site self-report as finding the process both enjoyable and inspiring.

  2. 2.

    https://github.com/tategallery/collection, available under the Creative Commons Public Domain CC0 licence.

  3. 3.

    http://www.tate.org.uk/art/artworks/wilkie-newsmongers-n00331.

  4. 4.

    https://en.oxforddictionaries.com/definition/grisette.

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Author information

Authors and Affiliations

  1. School of Electrical Engineering, University of Bristol, MVB, Woodland Road, Bristol, BSB 1UB, UK

    Emma L. Tonkin

  2. School of Geographical Sciences, University of Bristol, Bristol, UK

    Gregory J. L. Tourte

  3. King’s College London, London, UK

    Alastair Gill

Authors
  1. Emma L. Tonkin
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  2. Gregory J. L. Tourte
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  3. Alastair Gill
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Correspondence to Emma L. Tonkin .

Editor information

Editors and Affiliations

  1. Delft University of Technology, Delft, The Netherlands

    Arnold Vermeeren

  2. NHTV Breda University of Applied Sciences, Breda, The Netherlands

    Licia Calvi

  3. Loughborough University London, London, United Kingdom

    Amalia Sabiescu

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Tonkin, E.L., Tourte, G.J.L., Gill, A. (2018). Crowd Mining Applied to Preservation of Digital Cultural Heritage. In: Vermeeren, A., Calvi, L., Sabiescu, A. (eds) Museum Experience Design. Springer Series on Cultural Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-58550-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-58550-5_6

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