Advertisement

Extracting Emergent Semantics from Large-Scale User-Generated Content

Conference paper
  • 758 Downloads
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 150)

Abstract

This paper presents a survey of novel technologies for uncovering implicit knowledge through the analysis of user-contributed content in Web2.0 applications. The special features of emergent semantics are herein described, along with the various dimensions that the techniques should be able to handle. Consequently a series of application domains is given where the extracted information can be consumed. The relevant techniques are reviewed and categorised according to their capability for scaling, multi-modal analysis, social networks analysis, semantic representation, real-time and spatio-temporal processing. A showcase of such an emergent semantics extraction application, namely ClustTour, is also presented, and open issues and future challenges in this new field are discussed.

Keywords

emergent semantics social media analysis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Work, D., Blandin, S., Piccoli, B., Bayen, A.: A traffic model for velocity data assimilation. Applied Mathematics Research eXpress (2010)Google Scholar
  2. 2.
    Torres, L.H.: Citizen sourcing in the public interest. KM4D Journal 3(1), 134–145 (2007)Google Scholar
  3. 3.
    iSpot, your place to share nature, http://ispot.org.uk/
  4. 4.
  5. 5.
  6. 6.
  7. 7.
    Malone, T.W., Klein, M.: Harnessing Collective Intelligence to Address Global Climate Change. Innovations 2(3), 15–26 (2007)CrossRefGoogle Scholar
  8. 8.
    Kemp, C., Shafto, P., Berke, A., Tenenbaum, J.B.: Combining causal and similarity-based reasoning. In: Advances in Neural Information Processing Systems, vol. 19 (in Press)Google Scholar
  9. 9.
    Kennedy, L.S., Naaman, M., Ahern, S., Nair, R., Rattenbury, T.: How Flickr helps us make sense of the world: context and content in community-contributed media collections. ACM Multimedia, 631–640 (2007)Google Scholar
  10. 10.
    Quack, T., Leibe, B., Van Gool, L.: World-scale mining of objects and events from community photo collections. In: Proc. Int. Conf. on Content-Based Image and Video Retrieval, pp. 47–56 (2008)Google Scholar
  11. 11.
    Papadopoulos, S., Zigkolis, C., Kompatsiaris, Y., Vakali, A.: Cluster-based Landmark and Event Detection on Tagged Photo Collections. IEEE Multimedia 18(1), 52–63 (2011)CrossRefGoogle Scholar
  12. 12.
    Hollenstein, L., Purves, R.S.: Exploring place through user- generated content: using Flickr to describe city cores. Journal of Spatial Information Science (2009)Google Scholar
  13. 13.
    Girardin, F., Calabrese, F., Dal Fiore, F., Ratti, C., Blat, J.: Digital footprinting: Uncovering tourists with user-generated content. IEEE Pervasive Computing 7(4), 36–43 (2008)CrossRefGoogle Scholar
  14. 14.
    Kalantidis, Y., Tolias, G., Avrithis, Y., Phinikettos, M., Spyrou, E., Mylonas, P., Kollias, S.: VIRaL: Visual Image Retrieval and Localization. Multimedia Tools Appl. 51(2), 555–592 (2011)CrossRefGoogle Scholar
  15. 15.
    Nanopoulos, A., Gabriel, H.-H., Spiliopoulou, M.: Spectral Clustering in Social-Tagging Systems. In: Vossen, G., Long, D.D.E., Yu, J.X. (eds.) WISE 2009. LNCS, vol. 5802, pp. 87–100. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  16. 16.
    Specia, L., Motta, E.: Integrating Folksonomies with the Semantic Web. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 624–639. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  17. 17.
    Diederich, J., Iofciu, T.: Finding communities of practice from user profiles based on folksonomies. In: Proceedings of the 1st International Workshop on Building Technology Enhanced Learning Solutions for Communities of Practice, TEL-CoPs 2006 (2006)Google Scholar
  18. 18.
    Schifanella, R., Barrat, A., Cattuto, C., Markines, B., Menczer, F.: Folks in folksonomies: social link prediction from shared metadata. In: WSDM 2010: Proc. 3rd ACM Int. Conference on Web Search and Data Mining, pp. 271–280. ACM Press, New York (2010)Google Scholar
  19. 19.
    Au Yeung, C.M., Gibbins, N., Shadbolt, N.: A study of user profile generation from folksonomies. In: SWKM (2008)Google Scholar
  20. 20.
    Gemmell, J., Shepitsen, A., Mobasher, B., Burke, R.: Personalizing Navigation in Folksonomies Using Hierarchical Tag Clustering. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2008. LNCS, vol. 5182, pp. 196–205. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  21. 21.
    Au Yeung, C.M., Gibbins, N., Shadbolt, N.: Contextualising tags in collaborative tagging systems. In: HT 2009: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia, pp. 251–260. ACM Press, New York (2009)CrossRefGoogle Scholar
  22. 22.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors. In: World Wide Web Conference (2010)Google Scholar
  23. 23.
    Signorini, A.: Swine Flu monitoring using twitter, http://compepi.cs.uiowa.edu/alessio/twitter-monitor-swine-flu/
  24. 24.
    Jin, X., Gallagher, A., Cao, L., Luo, J., Han, J.: The Wisdom of Social Multimedia: Using Flickr For Prediction and Forecast. In: MM 2010 Proceedings of the International Conference on Multimedia, Firenze, Italy (2010)Google Scholar
  25. 25.
    Singh, V.K., Gao, M., Jain, R.: Social Pixels: Genesis and Evaluation. In: MM 2010 Proceedings of the international conference on Multimedia, Firenze, Italy (2010)Google Scholar
  26. 26.
    Corley, C.D., Mikler, A.R.: A computational framework to study public health epidemiology. In: International Joint Conferences on System Biology, Bioinformatics and Intelligent Computing (IJCBS 2009), Shanghai, China (2009)Google Scholar
  27. 27.
    Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S., Brilliant, L.: Detecting influenza epidemics using search engine query data. Nature 457, 1012–1014 (2009)CrossRefGoogle Scholar
  28. 28.
    Eysenbach, G.: Infodemiology: tracking flu-related searches on the web for syndromic surveillance. In: AMIA 2006 Symposium Proceedings, pp. 244–248 (2006)Google Scholar
  29. 29.
    Polgreen, P.M., Chen, Y., Pennock, D.M., Nelson, F.D.: Using internet searches for influenza surveillance. Clinical Infectious Diseases (Supplement), 1443–1448 (2008)Google Scholar
  30. 30.
    Johnson, H.A., Wagner, M.M., Hogan, W.R., Chapman, W., Olszewski, R.T., Dowling, J., Barnas, G.: Analysis of web access logs for surveillance of influenza. Stud. Health Technol. Inform. 107(Pt 2), 1202’lC6 (2004)Google Scholar
  31. 31.
    Hinze, A., Voisard, A.: Location and Time-Based Information Delivery in Tourism. In: Hadzilacos, T., Manolopoulos, Y., Roddick, J., Theodoridis, Y. (eds.) SSTD 2003. LNCS, vol. 2750, pp. 489–507. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  32. 32.
    Lin, Y.R., Candan, K.S., Sundaram, H., Xie, L.: Scent: Scalable compressed monitoring of evolving multi-relational social networks. In: Review at ACM Trans. on Multimedia Computing, Communications and Applications (2010)Google Scholar
  33. 33.
    Yang, Y.H., Wu, P.T., Lee, C.W., Lin, K.H., Hsu, W.H., Chen, H.H.: ContextSeer: context search and recommendation at query time for shared consumer photos. In: Proc. 16th ACM Int. Conf. on Multimedia (MM 2008), pp. 199–208. ACM, New York (2008)Google Scholar
  34. 34.
    Wu, X., Ngo, C.W., Hauptmann, A.G., Tan, H.K.: Real-Time Near-Duplicate Elimination for Web Video Search With Content and Context. IEEE Transactions on Multimedia 11(2), 196–207 (2009)CrossRefGoogle Scholar
  35. 35.
    Henrich, A., Lüdecke, V.: Determining geographic representations for arbitrary concepts at query time. In: Proceedings of the First International Workshop on Location and the Web (LOCWEB 2008), pp. 17–24. ACM, New York (2008)CrossRefGoogle Scholar
  36. 36.
    Papadopoulos, S., Zigkolis, C., Kapiris, S., Kompatsiaris, Y., Vakali, A.: City exploration by use of spatio-temporal analysis and clustering of user contributed photos. Demo Paper Accepted in ACM International Conference on Multimedia Retrieval, ICMR (2011)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Informatics & Telematics InstituteThessalonikiGreece

Personalised recommendations