Pipelines and their Compositions for Modeling and Analysis of controlled Online Networked Social Science Experiments

Chris Kuhlman, Madhav Marathe, Naren Ramakrishnan, Parang Saraf, Nathan Self

Abstract

There has been significant growth in online social science experiments in order to understand behavior at-scale, with finer-grained data collection. Considerable work is required to perform data analytics for custom experiments. We also seek to perform repeated networked experiments and modeling in an iterative loop. In this work, we design and build four composable and extensible automated software pipelines for (1) data analytics; (2) model property inference; (3) model/simulation; and (4) results analysis and comparisons between experimental data and model predictions. To reason about experiments and models, we design a formal data model. Our data model is for scenarios where subjects can repeat actions (from a set) any number of times over the game duration. Because the types of interactions and action sets are flexible, this class of experiments is large. Two case studies, on collective identity and complex contagion, illustrate use of the system.

Vanessa Cedeno-Mieles , Yihui Ren , Saliya Ekanayake, Brian J. Goode, Chris J. Kuhlman, Dustin Machi, Madhav V. Marathe, Henning S. Mortveit , Zhihao Hu, Xinwei Deng, Naren Ramakrishnan, Parang Saraf, Nathan Self, Noshir Contractor, Joshua M. Epstein, Michael W. Macy: Pipelines and their Compositions for Modeling and Analysis of controlled Online Networked SocialScience Experiments. WSC 2018: 774-785