  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.
