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

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.