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Sifting useful comments from Flickr Commons and YouTube

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Abstract

Cultural institutions are increasingly contributing content to social media platforms to raise awareness and promote use of their collections. Furthermore, they are often the recipients of user comments containing information that may be incorporated in their catalog records. However, not all user-generated comments can be used for the purpose of enriching metadata records. Judging the usefulness of a large number of user comments is a labor-intensive task. Accordingly, our aim was to provide automated support for curation of potentially useful social media comments on digital objects. In this paper, the notion of usefulness is examined in the context of social media comments and compared from the perspective of both end-users and expert users. A machine-learning approach is then introduced to automatically classify comments according to their usefulness. This approach uses syntactic and semantic comment features while taking user context into consideration. We present the results of an experiment we conducted on user comments collected from Flickr Commons collections and YouTube. A study is then carried out on the correlation between the commenting culture of a platform (YouTube and Flickr) with usefulness prediction. Our findings indicate that a few relatively straightforward features can be used for inferring useful comments. However, the influence of features on usefulness classification may vary according to the commenting cultures of platforms.

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Notes

  1. Library of Congress Flickr Pilot Project Report Summary http://www.loc.gov/rr/print/flickr_report_final_summary

  2. http://www.tate.org.uk/about/our-work/digital/social-media-directory

  3. Source: Library of Congress Flickr Pilot Project Report Summary, http://www.loc.gov/rr/print/flickr_report_final_summary.

  4. “The key goals of The Commons on Flickr are to first show users hidden treasures in the world’s public photography archives, and, second, to show how users’ input and knowledge can help make these collections even richer. Users are invited to help describe the photographs they discover in The Commons on Flickr, either by adding tags or leaving comments.” www.flickr.com/commons

  5. http://www.flickr.com/photos/library_of_congress/2850357813/comment72157607279573241

  6. http://www.flickr.com/photos/library_of_congress/2536790306/comment72157629444651496

  7. http://www.youtube.com/watch?v=Yka3M4uvUyo

  8. http://www.youtube.com/watch?v=d2qamDMs-3g

  9. http://alias-i.com/lingpipe/

  10. Flickr photo - April 15, 1901 http://www.flickr.com/photos/nlireland/6933777014/comment72157629836757055.

  11. Flickr photo - Jimmy Clabby. Boxing http://www.flickr.com/photos/library_of_congress/2163449292/comment72157603820313375

  12. http://gate.ac.uk

  13. Flickr photo—(Clara Runkel) Mrs. Oscar F. Grab http://www.flickr.com/photos/library_of_congress/6851810917/comment72157629260546153

  14. Flickr photo - Paris Exposition: Hungarian Pavilion, Paris, France, 1900 http://www.flickr.com/photos/brooklyn_museum/2486821878/comment72157613666119960

  15. PROHIBITION DOCUMENTARY. http://www.youtube.com/watch?v=OiYqFXmVAFg

  16. We also experimented with a different number of topics (10, 100, and 500) for training the LDA model. However, our results—discussed in the Experiments section—have shown that training the LDA model using 1,000 topics is a most influential setting

  17. YouTube Video – Martin Luther King, Jr. - Mini Bio http://www.youtube.com/watch?v=3ank52Zi_S0

  18. These are user accounts which have the pattern “Name (LOC P&P)” and use the Library of Congress logo

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Acknowledgments

This work was supported in part by a Marie Curie International Outgoing Fellowship within the 7th European Community Framework Program (PIOF-GA-2009-252206). We also thank members of the Library of Congress and especially Helena Zinkham for their insightful comments and advice on the result of the LOC project on Flickr Commons and the anonymous reviewers for feedback and suggestions.

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Authors and Affiliations

  1. Faculty of Computer Science, University of Vienna, Vienna, Austria

    Elaheh Momeni

  2. Austrian Institute of Technology, Vienna, Austria

    Bernhard Haslhofer

  3. Department of Software and Computer Technology, Delft University of Technology, Delft, The Netherlands

    Ke Tao & Geert-Jan Houben

Authors
  1. Elaheh Momeni
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  2. Bernhard Haslhofer
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  3. Ke Tao
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  4. Geert-Jan Houben
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Correspondence to Elaheh Momeni.

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Momeni, E., Haslhofer, B., Tao, K. et al. Sifting useful comments from Flickr Commons and YouTube. Int J Digit Libr 16, 161–179 (2015). https://doi.org/10.1007/s00799-014-0123-1

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