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Leveraging Massive User Contributions for Knowledge Extraction

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Next Generation Data Technologies for Collective Computational Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 352))

Abstract

The collective intelligence that emerges from the collaboration, competition, and co-ordination among individuals in social networks has opened up new opportunities for knowledge extraction. Valuable knowledge is stored and often “hidden” in massive user contributions, challenging researchers to find methods for leveraging these contributions and unfold this knowledge. In this chapter we investigate the problem of knowledge extraction from social media. We provide background information for knowledge extraction methods that operate on social media, and present three methods that use Flickr data to extract different types of knowledge namely, the community structure of tag-networks, the emerging trends and events in users tag activity, and the associations between image regions and tags in user tagged images. Our evaluation results show that despite the noise existing in massive user contributions, efficient methods can be developed to mine the semantics emerging from these data and facilitate knowledge extraction.

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Author information

Authors and Affiliations

  1. Informatics & Telematics Institute, Thermi, Thessaloniki, Greece

    Spiros Nikolopoulos, Elisavet Chatzilari, Eirini Giannakidou, Symeon Papadopoulos & Ioannis Kompatsiaris

  2. School of Electronic Engineering and Computer Science, Queen Mary University of London, UK

    Spiros Nikolopoulos

  3. Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, GU2 7XH, UK

    Elisavet Chatzilari

  4. Department of Computer Science, Aristotle University of Thessaloniki, Greece

    Eirini Giannakidou, Symeon Papadopoulos & Athena Vakali

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  1. Spiros Nikolopoulos
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  2. Elisavet Chatzilari
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  3. Eirini Giannakidou
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  4. Symeon Papadopoulos
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  5. Ioannis Kompatsiaris
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  6. Athena Vakali
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Editors and Affiliations

  1. School of Computing & Maths, University of Derby, DE22 1GB, Derby, United Kingdom (UK)

    Nik Bessis

  2. Dept de Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, Barcelona, Spain

    Fatos Xhafa

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Nikolopoulos, S., Chatzilari, E., Giannakidou, E., Papadopoulos, S., Kompatsiaris, I., Vakali, A. (2011). Leveraging Massive User Contributions for Knowledge Extraction. In: Bessis, N., Xhafa, F. (eds) Next Generation Data Technologies for Collective Computational Intelligence. Studies in Computational Intelligence, vol 352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20344-2_16

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  • DOI: https://doi.org/10.1007/978-3-642-20344-2_16

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