skip to main content
research-article

Camera Brand Congruence and Camera Model Propagation in the Flickr Social Graph

Published:01 October 2011Publication History
Skip Abstract Section

Abstract

Given that my friends on Flickr use cameras of brand X, am I more likely to also use a camera of brand X? Given that one of these friends changes her brand, am I likely to do the same? Do new camera models pop up uniformly in the friendship graph? Or do early adopters then “convert” their friends? Which factors influence the conversion probability of a user? These are the kind of questions addressed in this work. Direct applications involve personalized advertising in social networks.

For our study, we crawled a complete connected component of the Flickr friendship graph with a total of 67M edges and 3.9M users. 1.2M of these users had at least one public photograph with valid model metadata, which allowed us to assign camera brands and models to users and time slots. Similarly, we used, where provided in a user’s profile, information about a user’s geographic location and the groups joined on Flickr.

Concerning brand congruence, our main findings are the following. First, a pair of friends on Flickr has a higher probability of being congruent, that is, using the same brand, compared to two random users (27% vs. 19%). Second, the degree of congruence goes up for pairs of friends (i) in the same country (29%), (ii) who both only have very few friends (30%), and (iii) with a very high cliqueness (38%). Third, given that a user changes her camera model between March-May 2007 and March-May 2008, high cliqueness friends are more likely than random users to do the same (54% vs. 48%). Fourth, users using high-end cameras are far more loyal to their brand than users using point-and-shoot cameras, with a probability of staying with the same brand of 60% vs 33%, given that a new camera is bought. Fifth, these “expert” users’ brand congruence reaches 66% for high cliqueness friends. All these differences are statistically significant at 1%.

As for the propagation of new models in the friendship graph, we observe the following. First, the growth of connected components of users converted to a particular, new camera model differs distinctly from random growth. Second, the decline of dissemination of a particular model is close to random decline. This illustrates that users influence their friends to change to a particular new model, rather than from a particular old model. Third, having many converted friends increases the probability of the user to convert herself. Here differences between friends from the same or from different countries are more pronounced for point-and-shoot than for digital single-lens reflex users. Fourth, there was again a distinct difference between arbitrary friends and high cliqueness friends in terms of prediction quality for conversion.

References

  1. Ahn, Y.-Y., Han, S., Kwak, H., Moon, S., and Jeong, H. 2007. Analysis of topological characteristics of huge online social networking services. In Proceedings of the 16th International Conference on the World Wide Web (WWW). 835--844. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Algersheimer, R., Dholakia, U. M., and Herrmann, A. 2005. The social influence of brand community: Evidence from european car clubs. J. Market. 69, 19--34.Google ScholarGoogle ScholarCross RefCross Ref
  3. Backstrom, L., Huttenlocher, D., Kleinberg, J., and Lan, X. 2006. Group formation in large social networks: membership, growth, and evolution. In Proceedings of the 12th International Conference on Knowledge Discovery and Data Mining (KDD). 44--54. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Cha, M., Mislove, A., and Gummadi, K. P. 2009. A measurement-driven analysis of information propagation in the flickr social network. In Proceedings of the 18th International Conference on World Wide Web (WWW). 721--730. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Chaudhuri, A. and Holbrook, M. B. 2001. The chain of effects from brand trust and brand affect to brand performance: The role of brand loyalty. J. Market. 65, 81--93.Google ScholarGoogle ScholarCross RefCross Ref
  6. Crandall, D. J., Backstrom, L., Huttenlocher, D., and Kleinberg, J. 2009. Mapping the world’s photos. In Proceedings of the 18th International Conference on World Wide Web (WWW). 761--770. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Garg, N. and Weber, I. 2008. Personalized, interactive tag recommendation for flickr. In Proceedings of the Conference on Recommender Systems (RecSys). 67--74. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Kirk, R. E. 2007. Statistics: An Introduction 5th Ed. Wadsworth Publishing, Beverly, MA.Google ScholarGoogle Scholar
  9. Kleinberg, J. 2007. Cascading behavior in networks: Algorithmic and economic issues. In Algorithmic Game Theory, N. Nisan, T. Roughgarden, E. Tardos, and V. V. Vazirani Eds., Cambridge University Press, Chapter 24, 613--632.Google ScholarGoogle Scholar
  10. Leskovec, J., Adamic, L. A., and Huberman, B. A. 2007. The dynamics of viral marketing. ACM Trans. Web 1, 1, 5. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Leskovec, J. and Horvitz, E. 2008. Planetary-scale views on a large instant-messaging network. In Proceedings of the 17th International Conference on the World Wide Web (WWW). 915--924. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Leskovec, J., Singh, A., and Kleinberg, J. M. 2006. Patterns of influence in a recommendation network. In Proceedings of the 10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD). 380--389. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Marlow, C., Naaman, M., Boyd, D., and Davis, M. 2006. Ht06, tagging paper, taxonomy, Flickr, academic article, to read. In Proceedings of the 7th Conference on Hypertext and Hypermedia (HT). 31--40. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Mislove, A., Koppula, H. S., Gummadi, K. P., Druschel, P., and Bhattacharjee, B. 2008. Growth of the Flickr social network. In Proceedings of the 1st Workshop on Online Social Networks (WOSP). 25--30. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., and Bhattacharjee, B. 2007. Measurement and analysis of online social networks. In Proceedings of the 7th SIGCOMM Conference on Internet Measurement (IMC). 29--42. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Popescu, A. and Grefenstette, G. 2010. Mining user home location, travel patterns and gender from Flickr tags. In Proceedings of the 4th International AAAI Conference on Weblogs and Social Media (ICWSM). 307--310.Google ScholarGoogle Scholar
  17. Rattenbury, T., Good, N., and Naaman, M. 2007. Towards automatic extraction of event and place semantics from Flickr tags. In Proceedings of the 30th International SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 103--110. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Reingen, P., Foster, B., Brown, J. J., and Seidman, S. 1984. Brand congruence in interpersonal relations: A social network analysis. J. Consum. Res. 11, 3, 771--783.Google ScholarGoogle ScholarCross RefCross Ref
  19. Richardson, M. and Domingos, P. 2002. Mining knowledge-sharing sites for viral marketing. In Proceedings of the 8th International SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD). 61--70. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Sigurbjornsson, B. and van Zwol, R. 2008. Flickr tag recommendation based on collective knowledge. In Proceedings of the 17th International Conference on the World Wide Web (WWW). 327--336. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Singla, A. and Weber, I. 2009. Camera brand congruence in the Flickr social graph. In Proceedings of the 2nd International Conference on Web Search and Data Mining (WSDM). 252--261. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Singla, P. and Richardson, M. 2008. Yes, there is a correlation - from social networks to personal behavior on the web. In Proceedings of the 17th International Conference on the World Wide Web (WWW). 655--664. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Yang, W.-S., Dia, J.-B., Cheng, H.-C., and Lin, H.-T. 2006. Mining social networks for targeted advertising. In Proceedings of the Hawaii International Conference on System Sciences (HICSS) 6, 137a. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Camera Brand Congruence and Camera Model Propagation in the Flickr Social Graph

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      • Published in

        cover image ACM Transactions on the Web
        ACM Transactions on the Web  Volume 5, Issue 4
        October 2011
        154 pages
        ISSN:1559-1131
        EISSN:1559-114X
        DOI:10.1145/2019643
        Issue’s Table of Contents

        Copyright © 2011 ACM

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 1 October 2011
        • Accepted: 1 February 2011
        • Revised: 1 December 2010
        • Received: 1 February 2010
        Published in tweb Volume 5, Issue 4

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader
      About Cookies On This Site

      We use cookies to ensure that we give you the best experience on our website.

      Learn more

      Got it!