Data analysis and modeling pipelines for controlled networked social science experiments

Vanessa Cedeno-Mieles, Zhihao Hu, Yihui Ren, Xinwei Deng, Noshir Contractor, Saliya Ekanayake, Joshua M Epstein, Brian J Goode, Gizem Korkmaz, Chris J Kuhlman, Dustin Machi, Michael Macy, Madhav V Marathe, Naren Ramakrishnan, Parang Saraf and Nathan Self

PLOS ONE, 2020, vol. 15, issue 11, 1-58

Abstract: There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0242453

DOI: 10.1371/journal.pone.0242453

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