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Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Introduction to Information Retrieval
Introduction to Information Retrieval
Proceedings of the forty-first annual ACM symposium on Theory of computing
Kronecker Graphs: An Approach to Modeling Networks
The Journal of Machine Learning Research
Combined regression and ranking
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Random Structures & Algorithms
Modeling social networks through user background and behavior
WAW'11 Proceedings of the 8th international conference on Algorithms and models for the web graph
Technically Speaking: All A-Twitter
IEEE Spectrum
Learning social network embeddings for predicting information diffusion
Proceedings of the 7th ACM international conference on Web search and data mining
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The diffusion of information on online social and information networks has been a popular topic of study in recent years, but attention has typically focused on speed of dissemination and recall (i.e. the fraction of users getting a piece of information). In this paper, we study the complementary notion of the precision of information diffusion. Our model of information dissemination is "broadcast-based'', i.e., one where every message (original or forwarded) from a user goes to a fixed set of recipients, often called the user's ``friends'' or ``followers'', as in Facebook and Twitter. The precision of the diffusion process is then defined as the fraction of received messages that a user finds interesting. On first glance, it seems that broadcast-based information diffusion is a "blunt" targeting mechanism, and must necessarily suffer from low precision. Somewhat surprisingly, we present preliminary experimental and analytical evidence to the contrary: it is possible to simultaneously have high precision (i.e. is bounded below by a constant), high recall, and low diameter! We start by presenting a set of conditions on the structure of user interests, and analytically show the necessity of each of these conditions for obtaining high precision. We also present preliminary experimental evidence from Twitter verifying that these conditions are satisfied. We then prove that the Kronecker-graph based generative model of Leskovec et al. satisfies these conditions given an appropriate and natural definition of user interests. Further, we show that this model also has high precision, high recall, and low diameter. We finally present preliminary experimental evidence showing Twitter has high precision, validating our conclusion. This is perhaps a first step towards a formal understanding of the immense popularity of online social networks as an information dissemination mechanism.