Intent-Driven Behavioral Modeling during Cross-Border Epidemics

Santos, Eunice E.; Santos, Eugene; Korah, John; Thompson, Jeremy E.; Kim, Keumjoo; George, Riya; Gu, Qi; Jurmain, Jacob C.; Subramanian, S.; Wilkinson, John

doi:10.1109/passat/socialcom.2011.187

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…Santos et al [4] conducted a Cross-Border Epidemic Spread project to study why and under what circumstances would people be driven to cross the border both legally and illegally with respect to epidemic spread. In order to understand such human behavior as well as the intent, they employed the intent framework represented by BKBs to model people's reaction to the various events that took place during the pandemic in 2009.…”

Section: H1n1mentioning

confidence: 99%

“…Therefore, the characteristics of these events and their impact patterns are key to analyzing people's reactions. We apply our method on a series of intent systems (represented by BKBs) modeled in the paper [4] to show an example of how our approach can be used to analyze behavior of populations during H1N1 and their propensity to escape to neighboring countries, e.g. illegal migration to US.…”

Section: H1n1mentioning

confidence: 99%

“…In this paper, we use a probabilistic framework called Bayesian Knowledge Bases (BKBs) [9] to represent individual/group beliefs and attitudes, as it has been extensively used to model complex intent-driven scenarios [4] [6]. We propose a new modeling approach 1 called a Finite Fusion Model (FFM) for detecting and tracking hidden sources in a time-variant scenario that consists of a sequence of beliefs encoded in BKBs.…”

Section: Introductionmentioning

confidence: 99%

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Hidden Source Behavior Change Tracking and Detection

Santos

et al. 2012

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

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An important task of modeling complex social behaviors is to observe and understand individual/group beliefs and attitudes. These beliefs, however, are not stable and may change multiple times as people gain additional information/perceptions from various external sources, which in turn, may affect their subsequent behavior. To detect and track such influential sources is challenging, as they are often invisible to the public due to a variety of reasons -private communications, what one randomly reads or hears, and implicit social hierarchies, to name a few. Existing approaches usually focus on detecting distribution variations in behavioral data, but overlook the underlying reason for the variation. In this paper, we present a novel approach that models the belief change over time caused by hidden sources, taking into consideration the evolution of their impact patterns. Specifically, a finite fusion model is defined to encode the latent parameters that characterize the distribution of the hidden sources and their impact weights. We compare our work with two general mixture models, namely Gaussian Mixture Model and Mixture Bayesian Network. Experiments on both synthetic data and a real-world scenario show that our approach is effective on detecting and tracking hidden sources and outperforms existing methods.

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Section: H1n1mentioning

confidence: 99%

Section: H1n1mentioning

confidence: 99%