  Risk communication in times of disasters is complex, involving rapid and
diverse communication in social networks (i.e., public and/or private agencies;
local residents) as well as limited mobilization capacity and operational
constraints of physical infrastructure networks. Despite a growing literature
on infrastructure interdependencies and co-dependent social-physical systems,
an in-depth understanding of how risk communication in online social networks
weighs into physical infrastructure networks during a major disaster remains
limited, let alone in compounding risk events. This study analyzes large-scale
datasets of crisis mobility and activity-related social interactions and
concerns available through social media (Twitter) for communities that were
impacted by an ice storm (Oct. 2020) in Oklahoma. Compounded by the COVID-19
pandemic, Oklahoma residents faced this historic ice storm (Oct. 26, 2020-Oct.
29, 2020) that caused devastating traffic impacts (among others) due to
excessive ice accumulation. By using the recently released academic Application
Programming Interface (API) by Twitter that provides complete and unbiased
data, geotagged tweets (approx. 210K) were collected covering the entire state
of Oklahoma, and ice storm-related tweets (approx. 14.2K) were considered.
First, the study uses natural language processing and text quantification
techniques to translate crisis narratives (i.e., tweets). Next, geo-tagged
tweets are mapped into co-located road networks using traditional GIS
techniques. Finally, insights are generated using network science theories and
quantified social narratives to interpret different elements of road networks
(e.g., local roads, freeways, etc.) for the Oklahoma communities that were
impacted by the ice storm event during the pandemic.

Social and Information Networks