Social relevance: toward understanding the impact of the individual in an information cascade

Robert T. Hall; Joshua S. White; Jeremy Fields

doi:10.1117/12.2222888

12 May 2016 Social relevance: toward understanding the impact of the individual in an information cascade

Robert T. Hall, Joshua S. White, Jeremy Fields

Proceedings Volume 9826, Cyber Sensing 2016; 98260C (2016) https://doi.org/10.1117/12.2222888
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

Information Cascades (IC) through a social network occur due to the decision of users to disseminate content. We define this decision process as User Diffusion (UD). IC models typically describe an information cascade by treating a user as a node within a social graph, where a node’s reception of an idea is represented by some activation state. The probability of activation then becomes a function of a node’s connectedness to other activated nodes as well as, potentially, the history of activation attempts. We enrich this Coarse-Grained User Diffusion (CGUD) model by applying actor type logics to the nodes of the graph. The resulting Fine-Grained User Diffusion (FGUD) model utilizes prior research in actor typing to generate a predictive model regarding the future influence a user will have on an Information Cascade. Furthermore, we introduce a measure of Information Resonance that is used to aid in predictions regarding user behavior.

Citation Download Citation

Robert T. Hall, Joshua S. White, and Jeremy Fields "Social relevance: toward understanding the impact of the individual in an information cascade", Proc. SPIE 9826, Cyber Sensing 2016, 98260C (12 May 2016); https://doi.org/10.1117/12.2222888

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