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Camera brand congruence in the Flickr social graph

Published: 09 February 2009 Publication History

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? 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. Camera brands and models were assigned to users and time slots according to the model specific meta data pertaining to their images taken during these 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.
Our main findings are the following. First, a pair of friends on Flickr has a significantly higher probability of being congruent, i.e., 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.
To the best of our knowledge this is the first time that the phenomenon of brand congruence is studied for hundreds of thousands of users and over a period of two years.

References

[1]
Y.-Y. Ahn, S. Han, H. Kwak, S. Moon, and H. Jeong. Analysis of topological characteristics of huge online social networking services. In 16th International Conference on the World Wide Web (WWW), pages 835--844, 2007.
[2]
R. Algersheimer, U. M. Dholakia, and A. Herrmann. The social influence of brand community: Evidence from european car clubs. Journal of Marketing, 69:19--34, 2005.
[3]
L. Backstrom, D. Huttenlocher, J. Kleinberg, and X. Lan. Group formation in large social networks: membership, growth, and evolution. In 12th International Conference on Knowledge Discovery and Data Mining (KDD), pages 44--54, 2006.
[4]
A. Chaudhuri and M. B. Holbrook. The chain of effects from brand trust and brand affect to brand performance: The role of brand loyalty. Journal of Marketing, 65:81--93, 2001.
[5]
N. Garg and I. Weber. Personalized, interactive tag recommendation for flickr. In Conference on Recommender Systems (RecSys), pages 67--74, 2008.
[6]
J. Kleinberg. Cascading behavior in networks: algorithmic and economic issues. In N. Nisan, T. Roughgarden, E. Tardos, and V. V. Vazirani, editors, Algorithmic Game Theory, chapter 24. Cambridge University Press, 2007.
[7]
J. Leskovec, L. A. Adamic, and B. A. Huberman. The dynamics of viral marketing. ACM Transanctions on the Web, 1(1):5, 2007.
[8]
J. Leskovec and E. Horvitz. Planetary-scale views on a large instant-messaging network. In 17th International Conference on the World Wide Web (WWW), pages 915--924, 2008.
[9]
J. Leskovec, A. Singh, and J. M. Kleinberg. Patterns of influence in a recommendation network. In Advances in Knowledge Discovery and Data Mining, 10th Pacific-Asia Conference (PAKDD), pages 380--389, 2006.
[10]
C. Marlow, M. Naaman, D. Boyd, and M. Davis. Ht06, tagging paper, taxonomy, flickr, academic article, to read. In 7th Conference on Hypertext and Hypermedia (HT), pages 31--40, 2006.
[11]
A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In 7th SIGCOMM Conference on Internet measurement (IMC), pages 29--42, 2007.
[12]
T. Rattenbury, N. Good, and M. Naaman. Towards automatic extraction of event and place semantics from flickr tags. In 30th International SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pages 103--110, 2007.
[13]
P. Reingen, B. Foster, J. J. Brown, and S. Seidman. Brand congruence in interpersonal relations: A social network analysis. Journal of Consumer Research, 11(3):771--783, 1984.
[14]
M. Richardson and P. Domingos. Mining knowledge-sharing sites for viral marketing. In 8th International SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), pages 61--70, 2002.
[15]
B. Sigurbjornsson and R. van Zwol. Flickr tag recommendation based on collective knowledge. In 17th International Conference on the World Wide Web (WWW), pages 327--336, 2008.
[16]
P. Singla and M. Richardson. Yes, there is a correlation - from social networks to personal behavior on the web. In 17th International Conference on the World Wide Web (WWW), pages 655--664, 2008.
[17]
W.-S. Yang, J.-B. Dia, H.-C. Cheng, and H.-T. Lin. Mining social networks for targeted advertising. Hawaii International Conference on System Sciences (HICSS), 6:137a, 2006.

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cover image ACM Conferences
WSDM '09: Proceedings of the Second ACM International Conference on Web Search and Data Mining
February 2009
314 pages
ISBN:9781605583907
DOI:10.1145/1498759
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 09 February 2009

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Author Tags

  1. Flickr
  2. brand congruence
  3. brand loyalty
  4. social network

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  • (2017)Capturing the Gist(s) of Image Sets Associated with Chinese Cities through Related Tags Networks on Flickr®Social Media Listening and Monitoring for Business Applications10.4018/978-1-5225-0846-5.ch011(245-315)Online publication date: 2017
  • (2016)An Exploration of Fetish Social Networks and CommunitiesProceedings of the 12th International Conference and School on Advances in Network Science - Volume 956410.1007/978-3-319-28361-6_17(195-204)Online publication date: 11-Jan-2016
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  • (2011)Camera Brand Congruence and Camera Model Propagation in the Flickr Social GraphACM Transactions on the Web10.1145/2019643.20196475:4(1-25)Online publication date: 1-Oct-2011
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