  Influence diffusion has been central to the study of propagation of
information in social networks, where influence is typically modeled as a
binary property of entities: influenced or not influenced. We introduce the
notion of attitude, which, as described in social psychology, is the degree by
which an entity is influenced by the information. We present an information
diffusion model that quantifies the degree of influence, i.e., attitude of
individuals, in a social network. With this model, we formulate and study
attitude maximization problem. We prove that the function for computing
attitude is monotonic and sub-modular, and the attitude maximization problem is
NP-Hard. We present a greedy algorithm for maximization with an approximation
guarantee of $(1-1/e)$. Using the same model, we also introduce the notion of
"actionable" attitude with the aim to study the scenarios where attaining
individuals with high attitude is objectively more important than maximizing
the attitude of the entire network. We show that the function for computing
actionable attitude, unlike that for computing attitude, is non-submodular and
however is \emph{approximately submodular}. We present approximation algorithm
for maximizing actionable attitude in a network. We experimentally evaluated
our algorithms and study empirical properties of the attitude of nodes in
network such as spatial and value distribution of high attitude nodes.
