Measuring the Impact of Influence on Individuals: Roadmap to Quantifying Attitude

Date
2020-10-26T03:21:29Z
Authors
Fu, Xiaoyun
Padmanabhan, Madhavan
Kumar, Raj Gaurav
Basu, Samik
Pavan, A.
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Copyright 2020 IEEE
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Sociology
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Computer Science
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SociologyComputer Science
Abstract
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.
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The following is a manuscript of an article published as Fu, Xiaoyun, Madhavan Padmanabhan, Raj Gaurav Kumar, Samik Basu, Shawn Dorius, and A. Pavan. "Measuring the Impact of Influence on Individuals: Roadmap to Quantifying Attitude." In 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 227-231. IEEE Computer Society, 2020. Posted with permission.
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