The social media genome: modeling individual topic-specific behavior in social media

Authors:
Petko Bogdanov;Michael Busch;Jeff Moehlis;Ambuj K. Singh;Boleslaw K. Szymanski
Affiliations:
University of California Santa Barbara, Santa Barbara, CA;University of California Santa Barbara, Santa Barbara, CA;University of California Santa Barbara, Santa Barbara, CA;University of California Santa Barbara, Santa Barbara, CA;Rensselaer Polytechnic Institute, Troy, NY
Venue:
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Year:
2013

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Abstract

Information propagation in social media depends not only on the static follower structure but also on the topic-specific user behavior. Hence novel models incorporating dynamic user behavior are needed. To this end, we propose a model for individual social media users, termed a genotype. The genotype is a per-topic summary of a user's interest, activity and susceptibility to adopt new information. We demonstrate that user genotypes remain invariant within a topic by adopting them for classification of new information spread in large-scale real networks. Furthermore, we extract topic-specific influence backbone structures based on information adoption and show that they differ significantly from the static follower network. When employed for influence prediction of new content spread, our genotype model and influence backbones enable more than 20% improvement, compared to purely structural features. We also demonstrate that knowledge of user genotypes and influence backbones allow for the design of effective strategies for latency minimization of topic-specific information spread.