Social sensing: towards social media as a sensor for resilience in power systems and other critical infrastructures

ABSTRACT

Improving Critical Infrastructure (CI) resilience is a key challenge facing modern society. Increased integration of sensors into infrastructure systems, combined with modern computational capabilities, provides an opportunity to develop novel data-driven methods for improving resilience.  Social media serves as a promising data source for such methods, as it has become widely used for information dissemination. This paper aims to demonstrate the value of social media for CI resilience by using this novel data source to model CI behaviors at higher spatiotemporal resolutions than previously shown. We apply this approach, which focuses on statistical analysis and forecasting methods, to a case study of Hurricane Sandy using publicly available Twitter data and power system data for the New York Independent System Operator (NYISO). We find evidence of statistically significant correlations between Twitter and power system data, and develop models for forecasting future behaviors in the NYISO power system using these data.

KEYWORDS:

• Critical infrastructures
• resilience
• power systems
• social media
• Hurricane Sandy

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was partially supported by the Institute for Sustainability, Energy, and Environment (iSEE) at University of Illinois at Urbana-Champaign. The views in this document are those of the authors and do not reflect the official policy or position of the United States Air Force, Department of Defense, or the U.S. Government.

Notes on contributors

Jacob Heglund

Jacob Heglund is a graduate research assistant in the Department of Aerospace Engineering at the University of Illinois Urbana-Champaign, Urbana, IL.  His research interests are in the areas of critical infrastructure resilience, applications of statistical methods and machine learning, and reinforcement learning.

Kenneth M. Hopkinson

Kenneth M. Hopkinson is a Professor of Computer Science and the Head of the Department of Electrical and Computer Engineering at the Air Force Institute of Technology in Dayton, Ohio. His research interests include networks, security, cryptography, remote sensing, sensor fusion, critical infrastructure protection, and space applications.

Huy T. Tran

Huy T. Tran is a Research Assistant Professor in the Aerospace Engineering department at the University of Illinois at Urbana-Champaign, Urbana, IL. His current research focuses on developing algorithms for adaptive autonomy in multi-agent systems and prediction in complex systems, such as transportation and other infrastructure systems.