2026 Spring Seminar Series Seminar: Branda Nowell NC State University

Seeing Systems at Scale: Social Sensing and Big Data Approaches to Gaining Insight into System-Oriented Networks

Abstract: Remote sensing is the science of collecting information about a phenomenon without being in direct physical contact with it.  The sub-field of social sensing is the practice of using human-generated data to detect, interpret and understand social conditions indirectly. In this paper, we present a methodology for leveraging big data techniques and social sensing to gain insight into system-oriented networks. Scholars have long been interested in understanding the interconnected web of actors engaged in, and relevant to complex problem and policy domains. In 2022, Nowell and Milward introduced the term system-oriented networks (SONs) as an over-arching taxonomic classification describing a group of actors defined by boundary conditions set by the researcher around a system of interest within a defined geography. Referred to in the past by many names reflecting different sub-types and disciplinary traditions including policy networks, institutional ecosystems, governance networks, and community networks, the core assumption underlying research on SONs is that the observed outcomes in community and policy settings are often the product of a complex set of actions and interactions by a collection of institutional actors operating at different scales and representing diverse interests (Klijn and Koppenjan, 2014; Koliba et al, 2017). However, because of their complex, multi-level and dynamic nature, methodological challenges in studying SONs abound and theorizing about SONs has outpaced methodological advancements. Key methodological challenges include ambiguous boundary determinations, near population level data requirements for network level analytics, missing data, data collection costs when seeking to represent large geographies, survey burden associated with network roster data techniques, and the dynamic nature of networks leading to the need for feasible tracking of change over time (for discussion see Taylor & Nowell, 2024). While limited in application to date, large data analytic techniques offer promise as a complement to existing case study approaches for studying SONs. In this paper, we review the core analytical challenges of studying SONs and investigate what insights into the institutional make up and organization of SONs can be captured through remote social sensing using big data analytic techniques. Analytical opportunities as well as challenges are discussed.