Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systems
Author(s)Pan, Wei; Dong, Wen; Cebrian, Manuel; Kim, Taemie Jung; Fowler, James H.; Pentland, Alex Paul; ... Show more Show less
MetadataShow full item record
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.
DepartmentMassachusetts Institute of Technology. Media Laboratory; Program in Media Arts and Sciences (Massachusetts Institute of Technology)
IEEE Signal Processing Magazine
Institute of Electrical and Electronics Engineers (IEEE)
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.
Author's final manuscript