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Capturing Trust in Social Web Applications

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Part of the book series: Human–Computer Interaction Series ((HCIS))

Abstract

The Social Web constitutes a shift in information flow from the traditional Web. Previously, content was provided by the owners of a website, for consumption by the end-user. Nowadays, these websites are being replaced by Social Web applications which are frameworks for the publication of user-provided content. Traditionally, Web content could be ‘trusted’ to some extent based on the site it originated from. Algorithms such as Google’s PageRank were (and still are) used to compute the importance of a website, based on analysis of underlying link topology. In the Social Web, analysis of link topology merely tells us about the importance of the information framework which hosts the content. Consumers of information still need to know about the importance/reliability of the content they are reading, and therefore about the reliability of the producers of that content. Research into trust and reputation of the producers of information in the Social Web is still very much in its infancy. Every day, people are forced to make trusting decisions about strangers on the Web based on a very limited amount of information. For example, purchasing a product from an eBay seller with a ‘reputation’ of 99%, downloading a file from a peer-to-peer application such as Bit-Torrent, or allowing Amazon.com tell you what products you will like. Even something as simple as reading comments on a Web-blog requires the consumer to make a trusting decision about the quality of that information. In all of these example cases, and indeed throughout the Social Web, there is a pressing demand for increased information upon which we can make trusting decisions. This chapter examines the diversity of sources from which trust information can be harnessed within Social Web applications and discusses a high level classification of those sources. Three different techniques for harnessing and using trust from a range of sources are presented. These techniques are deployed in two sample Social Web applications—a recommender system and an online auction. In all cases, it is shown that harnessing an increased amount of information upon which to make trust decisions greatly enhances the user experience with the Social Web application.

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References

  1. Paolo Avesani, Paolo Massa, and Roberto Tiella. A trust-enhanced recommander system application. Molesking. In SAC ‘05: Proceedings of the 2005 ACM symposium onApplied computing, pages 1589–1593 ACM Press, New York, NY, USA, 2005.

    Google Scholar 

  2. Albert-Laszlo Barabási. Linked: How E verthing Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life. Plume Books, April 2003.

    Google Scholar 

  3. Terry Bossomaier. Linked: The new science of networks by albert-laszlo barabási. Artif. Life, 11(3):401–402, 2005.

    Article  Google Scholar 

  4. deSola Pool and Manfred Kochen. Contacts and influence. Social Networks, 1(1):5–51, 1978.

    Article  MathSciNet  Google Scholar 

  5. Malcolm Galdwell. The Tipping Point: How Little Things Can Make a Big Difference. Time Warner Books UK, January 2002.

    Google Scholar 

  6. Jennifer Ann Golbeck. Computing and applying trust in Web-based social networks. PhD thesis, Univeristy of Maryland at College Park, College Park, MD, USa, 2005. Chair-James Hendler.

    Google Scholar 

  7. Andun Josang, Roslan Ismail, and Colin Boyd. A survey of trust and reputation systems for online service provision.

    Google Scholar 

  8. Audun Jøsang, Elizabeth Gray, and Michael Kinateder. Analysing Topologies of Transitive Trust. In Theo Dimitrakos and Fabio Matrinelli, editors, Proceedings of the First International Workshop on Formal Aspects in Security and Trust (FAST2003), pages 9-22, Pisa, Italy, September 2003.

    Google Scholar 

  9. Audan Jøsang, Elizabeth Gray, and Michael Kinanteder. Simplification and Analysis of Transitive Trust Networks. Web Intelligence and Agent Systems: An International Journal, pages 1-1, September 2005, ISBN ISSN: 1570-1263.

    Google Scholar 

  10. Cliff A.C. Lampe, Nicole Ellison, and Charles Steinfield. A familiar face(book): profile elements as signals in an online social network. In CHI ’07: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 435-444 ACM Press, New York, NY, USA, 2007.

    Google Scholar 

  11. S. Marsh. Formalising trust as a computational concept. Ph.D. Thesis. Department of Mathematics and Computer Science, University of Stirling, 1994.

    Google Scholar 

  12. Paolo Massa and Paolo Avesani. Trust-aware collaborative filtering for recommender systems. Proceedings of International Conference on Cooperative Information Systems, Agia Napa, Cyprus, 25 Oct 29, 2004.

    Google Scholar 

  13. Paolo Massa and Bobby Bhattacharjee. Using trust in recommender systems: an experimental analysis. 2nd International Conference on Trust Management, Oxford, England, 2004.

    Google Scholar 

  14. S. Milgram. The small world problem. Psychology Today, 1:61-67, May 1967.

    Google Scholar 

  15. Bradley N. Miller, Istvan Albert, Shyong K. Lam, Joseph A. Konstan, and John Ridel. Movielens unplugged: experiences with an occasinally connected recommender systems. In IUI ’03: Proceedings of the 8th international conference on Intelligent user interfaces, pages 263-266, ACM Press, New York, NY, USA, 2003.

    Google Scholar 

  16. John O’Donovan, Brynjar Gretatsson, Barry Symth, and Tobias Hollerer. Peerchooser: Visual interactive recommendation. International Conference and Human Interaction (CHI’08), 2008.

    Google Scholar 

  17. M. F. Porter. An algorithm for suffix stirpping. Readings in information retrieval, pages 313-316, 1997.

    Google Scholar 

  18. Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Ridel. Grouplens: An open architecture for collaborative filtering of netnews. In Proceddings of ACM CSCW’94 Conference on Computer-Supported Cooperative Work, pages 175-186, 1994.

    Google Scholar 

  19. Paul Resnick and Richard Zeckhauser. Trust among strangers in internet transactions: Empirical analysis of ebay’s reputation system. The Economics of the Internet and E-Commerce. Volume 11 of Advances in Applied Microeconomics., December 2002.

    Google Scholar 

  20. Badrul M. Sarwar, George Karypis, Joseph A. Konstan, and John Reidl. Item-based collaborative filtering recommendation algorithms. In World Wide Web, pages 285-295, 2001.

    Google Scholar 

  21. Rashmi Sinha and Kirsten Swearingen. The role of transparency in recommender systems. In CHI ’02 extended abstracts on Human factors in computing systems, pages 830-831. ACM Press, 2002.

    Google Scholar 

  22. Cai-Nicolas Ziegler and Georg Lausen. Propagation models for trust and distrust in social networks. Information Systems Frontiers, 7(4–5): 337-358, 2005.

    Google Scholar 

  23. Cai-Nicolas Ziegler and Michal Skubacz. Towards automated reputation and brand monitoring on the Web. In Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intleligence, pages 1006-1070, IEEE Computer Society Press, Hong Kong, December 2006.

    Google Scholar 

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© 2009 Springer-Verlag London Limited

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O’Donovan, J. (2009). Capturing Trust in Social Web Applications. In: Golbeck, J. (eds) Computing with Social Trust. Human–Computer Interaction Series. Springer, London. https://doi.org/10.1007/978-1-84800-356-9_9

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  • DOI: https://doi.org/10.1007/978-1-84800-356-9_9

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84800-355-2

  • Online ISBN: 978-1-84800-356-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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