Skip to main content
SpringerLink
Log in
Book cover

International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management

IC3K 2014: Knowledge Discovery, Knowledge Engineering and Knowledge Management pp 118–131Cite as

Determining the Relative Importance of Webpages Based on Social Signals Using the Social Score and the Potential Role of the Social Score in an Asynchronous Social Search Engine

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 553))

Abstract

There are many ways to determine the relative importance of webpages. Specifically, a method that has proven to be very successful in practice is to value a webpage based on its position in the hyperlinked graph of the web. However, there is no generally applicable algorithm to determine the value of webpages based on an arbitrary number social signals such as likes, tweets and shares. By taking such social signals into account a more democratic method arises to determine the value of webpages. In this article we propose an algorithm named the Social Score that takes into account an arbitrary number of social signals to determine the relative importance of a webpage. Also, we present a worldwide top fifty of webpages based on the Social Score. Last, the potential role of the Social Score in an asynchronous Social Search engine is evaluated.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bao, S., Xue, G., Wu, X., Yu, Y., Fei, B., Su, Z.: Optimizing web search using social annotations. In: Proceedings of the 16th International Conference on World Wide Web, pp 501–510. ACM (2007)

    Google Scholar 

  2. Berners-Lee, T., Hendler, J., Lassila, O., et al.: The semantic web. Sci. Am. 284(5), 28–37 (2001)

    Article  Google Scholar 

  3. Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1), 107–117 (1998)

    Article  Google Scholar 

  4. Chung, L., Nixon, B.A., Yu, E., Mylopoulos, J.: Non-functional requirements in software engineering, vol. 5. Springer Science & Business Media (2012)

    Google Scholar 

  5. Evans, B.M., Chi, E.H.: Towards a model of understanding social search. In: Proceedings of the 2008 ACM Conference on Computer Supported Cooperative Work, pp 485–494. ACM (2008)

    Google Scholar 

  6. Gerlitz, C., Helmond, A.: The like economy: social buttons and the data-intensive web. New Media Soc. 15(8), 1348–1365 (2013)

    Article  Google Scholar 

  7. Golder, S.A., Huberman, B.A.: Usage patterns of collaborative tagging systems. J. Inf. Sci. 32(2), 198–208 (2006)

    Article  Google Scholar 

  8. Golovchinsky, G., Pickens, J., Back, M.: A taxonomy of collaboration in online information seeking. arXiv preprint arXiv:0908.0704

  9. Heymann, P., Koutrika, G., Garcia-Molina, H.: Can social bookmarking improve web search? In: Proceedings of the International Conference on Web Search and Web Data Mining, pp 195–206. ACM (2008)

    Google Scholar 

  10. Hu, Y., Xin, G., Song, R., Hu, G., Shi, S., Cao, Y., Li, H.: Extraction from bodies of html documents and its application to web page retrieval. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 250–257. ACM (2005)

    Google Scholar 

  11. Joachims, T.: nbiased evaluation of retrieval quality using clickthrough data. In: SIGIR Workshop on Mathematical/Formal Methods in Information Retrieval, vol. 354. Citeseer (2002)

    Google Scholar 

  12. Latour, B., Woolgar, S.: Laboratory Life: The Social Construction of Scientific Facts. Princeton University Press, Princeton (1979)

    Google Scholar 

  13. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval, vol. 1. Cambridge University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

  14. Matthijs, N., Radlinski, F.: Personalizing web search using long term browsing history. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp 25–34. ACM (2011)

    Google Scholar 

  15. Noll, M.G., Meinel, C.: Web search personalization via social bookmarking and tagging. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 367–380. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  16. Ortega, F., Gonzalez-Barahona, J.M., Robles, G.: On the inequality of contributions to wikipedia. In: Proceedings of the 41st Annual Hawaii International Conference on System Sciences, pp 304–304. IEEE (2008)

    Google Scholar 

  17. Robey, D., Farrow, D.: User involvement in information system development: a conflict model and empirical test. Manage. Sci. 28(1), 73–85 (1982)

    Article  Google Scholar 

  18. Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)

    MATH  Google Scholar 

  19. Yanbe, Y., Jatowt, A., Nakamura, S., Tanaka, K.: Can social bookmarking enhance search in the web? In: Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries, pp 107–116. ACM (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Business Informatics, Utrecht University, Domplein 29, 3512 JE, Utrecht, Netherlands

    Marco Buijs & Marco Spruit

Authors
  1. Marco Buijs
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marco Spruit
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marco Buijs .

Editor information

Editors and Affiliations

  1. Instituto de Telecomunicações, Lisboa, Portugal

    Ana Fred

  2. Delft University of Technology, Delft, Zuid-Holland, The Netherlands

    Jan L. G. Dietz

  3. University of Madeira, Funchal, Portugal

    David Aveiro

  4. Henley Business School, University of Reading, Reading, United Kingdom

    Kecheng Liu

  5. INSTICC, Setubal, Portugal

    Joaquim Filipe

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Buijs, M., Spruit, M. (2015). Determining the Relative Importance of Webpages Based on Social Signals Using the Social Score and the Potential Role of the Social Score in an Asynchronous Social Search Engine. In: Fred, A., Dietz, J., Aveiro, D., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2014. Communications in Computer and Information Science, vol 553. Springer, Cham. https://doi.org/10.1007/978-3-319-25840-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-25840-9_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25839-3

  • Online ISBN: 978-3-319-25840-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation