Computational social science | Taglines

Dec 04

0

Abigail Jacobs

By Grace Li |

I am interested in how governance, communities, and inequality emerge in sociotechnical systems, and how the structure of sociotechnical systems encodes and reinforces these processes. To those ends, I develop empirical data and computational methods, focusing on latent variable models; statistical inference in networks; empirical design to study governance in organizations, platforms, and computational social systems; and causal inference and measurement in observational data.

Several sample projects:
> developing empirical populations of networks to infer social and ecological processes encoded in networks
> using probabilistic methods to infer the structure and dynamics of the illicit wildlife trade
> building from theory from political science, statistics, and education to disentangle issues of “bias” in computational systems

Sep 12

0

Paramveer Dhillon

By ljing |

My research interests span topics in Statistical Machine Learning, Computational Social Science, Natural Language Processing, and Field/Digital Experiments. Substantively, I am interested in understanding the impact of internet technologies on users by empirically studying their interactions with such systems. The research questions that I study are of both predictive as well as causal nature and I examine them by using data from text/natural language and social network domains.

The same network is displayed, parameterized by four different models of the distribution of influence and susceptibility over nodes, characterized by four types of nodes: low influence and low susceptibility nodes, high influence and low susceptibility nodes, high influence and high susceptibility nodes and low influence and high susceptibility nodes. The optimal seed nodes selected under each model are outlined in green. a, Baseline IC and LT models for which propagation properties are specified as constant (top) and the inverse of node degree (bottom), respectively. b, Baseline IC and LT models for which propagation properties are specified according to the assortative influence, assortative susceptibility, substitute influence–susceptibility (AAS) model. c–e, The same information as in b, but for the assortative influence, disassortative susceptibility, substitute influence–susceptibility (ADS; c), disassortative influence, disassortative susceptibility, substitute influence–susceptibility (DDS; d) and disassortative influence, assortative susceptibility, substitute influence–susceptibility (DAS; e) empirical influence models. Distributions of the frequency of the four types of nodes with different influence and susceptibility characterizations are displayed underneath each graph or model. Seed sets differ substantially across different parameterizations of the graph, implying vastly different influence maximization results for the different models of influence and susceptibility. **This figure is taken from the paper: “Social influence maximization under empirical influence models” by (Aral and Dhillon, Nature Human Behaviour 2018).**

MIDAS Faculty Research Pitch, Fall 2021

Sep 22

0

Ceren Budak

By Moira Dowling |

Ceren Budak is a Assistant Professor at the University of Michigan School of Information and her lie in the area of computational social science; a discipline at the intersection of computer science, statistics, and the social sciences. She is particularly interested in applying large scale data analysis techniques to study problems with social, political, and policy implications.

Mar 08

0

Margaret C. Levenstein

By boxcar-admin |

Margaret C. Levenstein, PhD, is the Director of ICPSR, Co-Director, Michigan Federal Statistical Research Data Center, Research Professor, School of Information, Research Professor, Survey Research Center, Institute for Social Research, and Adjunct Professor of Business Economics and Public Policy, Ross School of Business at the University of Michigan, Ann Arbor.