Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Social Networks for Targeted Advertising
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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ACM Transactions on the Web (TWEB)
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Proceedings of the 16th international conference on World Wide Web
Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
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Proceedings of the 17th international conference on World Wide Web
Yes, there is a correlation: - from social networks to personal behavior on the web
Proceedings of the 17th international conference on World Wide Web
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Proceedings of the 17th international conference on World Wide Web
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Proceedings of the first workshop on Online social networks
Personalized, interactive tag recommendation for flickr
Proceedings of the 2008 ACM conference on Recommender systems
Camera brand congruence in the Flickr social graph
Proceedings of the Second ACM International Conference on Web Search and Data Mining
A measurement-driven analysis of information propagation in the flickr social network
Proceedings of the 18th international conference on World wide web
Proceedings of the 18th international conference on World wide web
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PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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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.