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Geotag Propagation with User Trust Modeling

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Social Media Retrieval

Part of the book series: Computer Communications and Networks ((CCN))

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Abstract

The amount of information that people share on social networks is constantly increasing. People also comment, annotate, and tag their own content (videos, photos, notes, etc.), as well as the content of others. In many cases, the content is tagged manually. One way to make this time-consuming manual tagging process more efficient is to propagate tags from a small set of tagged images to the larger set of untagged images automatically. In such a scenario, however, a wrong or a spam tag can damage the integrity and reliability of the automated propagation system. Users may make mistakes in tagging, or irrelevant tags and content may be added maliciously for advertisement or self-promotion. Therefore, a certain mechanism insuring the trustworthiness of users or published content is needed. In this chapter, we discuss several image retrieval methods based on tags, various approaches to trust modeling and spam protection in social networks, and trust modeling in geotagging systems. We then consider a specific example of automated geotag propagation system that adopts a user trust model. The tag propagation in images relies on the similarity between image content (famous landmarks) and its context (associated geotags). For each tagged image, similar untagged images are found by the robust graph-based object duplicate detection, and the known tags are propagated accordingly. The user trust value is estimated based on a social feedback from the users of the photo-sharing system, and only tags from trusted users are propagated. This approach demonstrates that a practical tagging system significantly benefits from the intelligent combination of efficient propagation algorithm and a user-centered trust model.

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Notes

  1. 1.

    http://www.flickr.com

  2. 2.

    http://picasa.google.com

  3. 3.

    http://www.photobucket.com

  4. 4.

    http://www.facebook.com

  5. 5.

    http://plus.google.com

  6. 6.

    http://www.wikitravel.com

  7. 7.

    http://images.google.com

  8. 8.

    http://www.wikipedia.org

  9. 9.

    http://www.panoramio.com

  10. 10.

    http://www.zooomr.com

  11. 11.

    http://www.ebay.com

  12. 12.

    http://www.amazon.com

  13. 13.

    http://www.epinions.com

  14. 14.

    http://www.bibsonomy.org

  15. 15.

    http://www.delicious.com

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Acknowledgements

This work was supported by the Swiss National Foundation for Scientific Research in the framework of NCCR Interactive Multimodal Information Management (IM2), the Swiss National Science Foundation Grant “Multimedia Security” (number 200020-113709), and partially supported by the European Network of Excellence PetaMedia (FP7/2007-2011).

Author information

Authors and Affiliations

  1. Multimedia Signal Processing Group (MMSPG), École Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Vaud, Switzerland

    Ivan Ivanov, Peter Vajda, Pavel Korshunov & Touradj Ebrahimi

  2. School of Integrated Technology, Yonsei University, Incheon, 406-840, South Korea

    Jong-Seok Lee

Authors
  1. Ivan Ivanov
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  2. Peter Vajda
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  3. Jong-Seok Lee
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  4. Pavel Korshunov
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  5. Touradj Ebrahimi
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Corresponding author

Correspondence to Ivan Ivanov .

Editor information

Editors and Affiliations

  1. Department of Electronic Engineering, Queen Mary University of London, Mile End Road, London, E1 4NS, United Kingdom

    Naeem Ramzan

  2. Director of Product Innovation, Search, Netflix, Winchester Circle 100, Los Gatos, 95032, California, USA

    Roelof van Zwol

  3. School of Integrated Technology, Yonsei University, 162-1 Songdo-dong, Yeonsu-gu, Incheon, 406-840, Korea, Republic of (South Korea)

    Jong-Seok Lee

  4. Institut für Telekommunikationssysteme, Technische Universität Berlin, Einsteinufer 17, Berlin, 10587, Germany

    Kai Clüver

  5. Media Computing Group, Microsoft Research, 555 108th Ave NE, Bellevue, 98004, Washington, USA

    Xian-Sheng Hua

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Ivanov, I., Vajda, P., Lee, JS., Korshunov, P., Ebrahimi, T. (2013). Geotag Propagation with User Trust Modeling. In: Ramzan, N., van Zwol, R., Lee, JS., Clüver, K., Hua, XS. (eds) Social Media Retrieval. Computer Communications and Networks. Springer, London. https://doi.org/10.1007/978-1-4471-4555-4_13

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  • DOI: https://doi.org/10.1007/978-1-4471-4555-4_13

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