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Diffusion of information in social network has been the focus of intense research in the recent past decades due to its significant impact in shaping public discourse through group/individual influence. Existing research primarily models influence as a binary property of entities: influenced or not influenced. While this is a useful abstraction, it discards the notion of degree of influence, i.e., certain individuals may be influenced ``more'' than others. We introduce the notion of \emph{attitude}, which, as described in social psychology, is the degree by which an entity is influenced by the information. Intuitively, attitude captures the number of distinct neighbors of an entity influencing the latter. We present an information diffusion model (AIC 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 $(1-1/e)$. In the context of AIC model, we study two problems, with the aim to investigate the scenarios where attaining individuals with high attitude is objectively more important than maximizing the attitude of the entire network. In the first problem, we introduce the notion of \emph{actionable attitude}; intuitively, individuals with actionable attitude are likely to ``act'' on their attained attitude. 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. In the second problem, we consider identifying the number of individuals in the network with attitude above a certain value, a threshold. In this context, the function for computing the number of individuals with attitude above a given threshold induced by a seed set is \emph{neither submodular nor supermodular}. We present heuristics for realizing the solution to the problem. We experimentally evaluated our algorithms and studied empirical properties of the attitude of nodes in network such as spatial and value distribution of high attitude nodes. 

This article relies upon the recreancy theorem to empirically assess the extent to which people’s desires for technology efficacy, personal security, and social justice affect their trust in and support for government use of network surveillance as it is applied to local law enforcement and homeland security. The recreancy theorem complements technology adoption models in that it focuses upon public assessments of innovations as they are managed by societal institutions, thereby providing conceptual congruity between technology adoption and public assessments of institutional competency and integrity. Based upon the results of a social survey of 1488 adults living in the contiguous United States, the article expands our conceptual understanding of public opinions of network surveillance and empirically documents public demand for network surveillance that fosters goals of social justice more so than goals of self-interest.

 

Influence diffusion has been central to the study of the 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 the 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 but is approximately submodular. We present an approximation algorithm for maximizing actionable attitude in a network. We experimentally evaluated our algorithms and studied empirical properties of the attitude of nodes in the network such as spatial and value distribution of high attitude nodes.