  Recently, information transmission models motivated by the classical epidemic
propagation, have been applied to a wide-range of social systems, generally
assume that information mainly transmits among individuals via peer-to-peer
interactions on social networks. In this paper, we consider one more approach
for users to get information: the out-of-social-network influence. Empirical
analyses of eight typical events' diffusion on a very large micro-blogging
system, \emph{Sina Weibo}, show that the external influence has significant
impact on information spreading along with social activities. In addition, we
propose a theoretical model to interpret the spreading process via both
internal and external channels, considering three essential properties: (i)
memory effect; (ii) role of spreaders; and (iii) non-redundancy of contacts.
Experimental and mathematical results indicate that the information indeed
spreads much quicker and broader with mutual effects of the internal and
external influences. More importantly, the present model reveals that the event
characteristic would highly determine the essential spreading patterns once the
network structure is established. The results may shed some light on the
in-depth understanding of the underlying dynamics of information transmission
on real social networks.
