Evaluating the descriptive power of Instagram hashtags

  title={Evaluating the descriptive power of Instagram hashtags},
  author={Stamatios Giannoulakis and N. Tsapatsoulis},
  journal={J. Innov. Digit. Ecosyst.},
  • Stamatios Giannoulakis, N. Tsapatsoulis
  • Published 2016
  • Computer Science
  • J. Innov. Digit. Ecosyst.
  • Abstract Image tagging is an essential step for developing Automatic Image Annotation (AIA) methods that are based on the learning by example paradigm. [...] Key Result Results show that an average of 66% of the participants hashtag choices coincide with those suggested by the photo owners; thus, an initial evidence towards our hypothesis confirmation can be claimed.Expand Abstract
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    Publications referenced by this paper.
    Instagram Hashtags as Image Annotation Metadata
    • 10
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    Defining and Identifying Stophashtags in Instagram
    • 6
    Crowdsourcing annotation: Modelling keywords using low level features
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    • 1,037
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