Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systems
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 act...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2014
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| Online Access: | http://hdl.handle.net/1721.1/92443 https://orcid.org/0000-0002-8053-9983 |
| _version_ | 1826207451579416576 |
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| author | Dong, Wen Cebrian, Manuel Kim, Taemie Jung Fowler, James H. Pentland, Alex Paul Pan, Wei, Ph. D. Massachusetts Institute of Technology |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Dong, Wen Cebrian, Manuel Kim, Taemie Jung Fowler, James H. Pentland, Alex Paul Pan, Wei, Ph. D. Massachusetts Institute of Technology |
| author_sort | Dong, Wen |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:49:51Z |
| format | Article |
| id | mit-1721.1/92443 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:49:51Z |
| publishDate | 2014 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/924432022-09-28T16:27:51Z Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systems Dong, Wen Cebrian, Manuel Kim, Taemie Jung Fowler, James H. Pentland, Alex Paul Pan, Wei, Ph. D. Massachusetts Institute of Technology Massachusetts Institute of Technology. Media Laboratory Program in Media Arts and Sciences (Massachusetts Institute of Technology) Pan, Wei Dong, Wen Cebrian, Manuel Kim, Taemie Jung Pentland, Alex Paul 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. United States. Air Force Office of Scientific Research (Award FA9550-10-1-0122) United States. Army Research Laboratory (Cooperative Agreement W911NF-09-2-0053) 2014-12-22T18:53:59Z 2014-12-22T18:53:59Z 2012-02 Article http://purl.org/eprint/type/JournalArticle 1053-5888 http://hdl.handle.net/1721.1/92443 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. https://orcid.org/0000-0002-8053-9983 en_US http://dx.doi.org/10.1109/msp.2011.942737 IEEE Signal Processing Magazine Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Dong, Wen Cebrian, Manuel Kim, Taemie Jung Fowler, James H. Pentland, Alex Paul Pan, Wei, Ph. D. Massachusetts Institute of Technology Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systems |
| title | Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systems |
| title_full | Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systems |
| title_fullStr | Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systems |
| title_full_unstemmed | Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systems |
| title_short | Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systems |
| title_sort | modeling dynamical influence in human interaction using data to make better inferences about influence within social systems |
| url | http://hdl.handle.net/1721.1/92443 https://orcid.org/0000-0002-8053-9983 |
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