Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systems
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Pentland_Modeling dynamical.pdf
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606.32 KB
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Author(s)
Dong, Wen
Cebrian, Manuel
Kim, Taemie Jung
Fowler, James H.
Pentland, Alex Paul
Pan, Wei, Ph. D. Massachusetts Institute of Technology
Date Issued
February 2012
Journal
IEEE Signal Processing Magazine
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Wei Pan, Wen Dong, M. Cebrian, Taemie Kim, J. H. Fowler, and A. S. Pentland. “Modeling Dynamical Influence in Human Interaction: Using Data to Make Better Inferences About Influence Within Social Systems.” IEEE Signal Processing Magazine 29, no. 2 (March 2012): 77–86.
Version
Author's final manuscript
Abstract
How can we model influence between individuals in a social system, even when the network of interactions is unknown? In this article, we review the literature on the “influence model,” which utilizes independent time series to estimate how much the state of one actor affects the state of another actor in the system. We extend this model to incorporate dynamical parameters that allow us to infer how influence changes over time, and we provide three examples of how this model can be applied to simulated and real data. The results show that the model can recover known estimates of influence, it generates results that are consistent with other measures of social networks, and it allows us to uncover important shifts in the way states may be transmitted between actors at different points in time.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/msp.2011.942737
