The importance of the ability of predict trends in social media has been
growing rapidly in the past few years with the growing dominance of social
media in our everyday's life. Whereas many works focus on the detection of
anomalies in networks, there exist little theoretical work on the prediction of
the likelihood of anomalous network pattern to globally spread and become
"trends". In this work we present an analytic model the social diffusion
dynamics of spreading network patterns. Our proposed method is based on
information diffusion models, and is capable of predicting future trends based
on the analysis of past social interactions between the community's members. We
present an analytic lower bound for the probability that emerging trends would
successful spread through the network. We demonstrate our model using two
comprehensive social datasets - the "Friends and Family" experiment that was
held in MIT for over a year, where the complete activity of 140 users was
analyzed, and a financial dataset containing the complete activities of over
1.5 million members of the "eToro" social trading community.Comment: 6 Pages + Appendi
