Modeling individual topic-specific behavior and influence backbone networks in social media

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 content adoption and show that their structure differs significantly from the static follower network. We also find, at the population level using a simple contagion model, that hashtags of a known topic propagate at the greatest rate on backbone networks of the same topic. 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. It is also demonstrated that knowledge of user genotypes and influence backbones allows for the design of effective strategies for latency minimization of topic-specific information spread.