  Spreading processes are ubiquitous in natural and artificial systems. They
can be studied via a plethora of models, depending on the specific details of
the phenomena under study. Disease contagion and rumor spreading are among the
most important of these processes due to their practical relevance. However,
despite the similarities between them, current models address both spreading
dynamics separately. In this paper, we propose a general information spreading
model that is based on discrete time Markov chains. The model includes all the
transitions that are plausible for both a disease contagion process and rumor
propagation. We show that our model not only covers the traditional spreading
schemes, but that it also contains some features relevant in social dynamics,
such as apathy, forgetting, and lost/recovering of interest. The model is
evaluated analytically to obtain the spreading thresholds and the early time
dynamical behavior for the contact and reactive processes in several scenarios.
Comparison with Monte Carlo simulations shows that the Markov chain formalism
is highly accurate while it excels in computational efficiency. We round off
our work by showing how the proposed framework can be applied to the study of
spreading processes occurring on social networks.
