  Information propagation in online social networks is facilitated by two types
of influence - endogenous (peer) influence that acts between users of the
social network and exogenous (external) that corresponds to various external
mediators such as online news media. However, inference of these influences
from data remains a challenge, especially when data on the activation of users
is scarce. In this paper we propose a methodology that yields estimates of both
endogenous and exogenous influence using only a social network structure and a
single activation cascade. Our method exploits the statistical differences
between the two types of influence - endogenous is dependent on the social
network structure and current state of each user while exogenous is independent
of these. We evaluate our methodology on simulated activation cascades as well
as on cascades obtained from several large Facebook political survey
applications. We show that our methodology is able to provide estimates of
endogenous and exogenous influence in online social networks, characterize
activation of each individual user as being endogenously or exogenously driven,
and identify most influential groups of users.
