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Risk communication in times of disasters is complex, involving rapid anddiverse communication in social networks (i.e., public and/or private agencies;local residents) as well as limited mobilization capacity and operationalconstraints of physical infrastructure networks. Despite a growing literatureon infrastructure interdependencies and co-dependent social-physical systems,an in-depth understanding of how risk communication in online social networksweighs into physical infrastructure networks during a major disaster remainslimited, let alone in compounding risk events. This study analyzes large-scaledatasets of crisis mobility and activity-related social interactions andconcerns available through social media (Twitter) for communities that wereimpacted by an ice storm (Oct. 2020) in Oklahoma. Compounded by the COVID-19pandemic, Oklahoma residents faced this historic ice storm (Oct. 26, 2020-Oct.29, 2020) that caused devastating traffic impacts (among others) due toexcessive ice accumulation. By using the recently released academic ApplicationProgramming Interface (API) by Twitter that provides complete and unbiaseddata, geotagged tweets (approx. 210K) were collected covering the entire stateof Oklahoma, and ice storm-related tweets (approx. 14.2K) were considered.First, the study uses natural language processing and text quantificationtechniques to translate crisis narratives (i.e., tweets). Next, geo-taggedtweets are mapped into co-located road networks using traditional GIStechniques. Finally, insights are generated using network science theories andquantified social narratives to interpret different elements of road networks(e.g., local roads, freeways, etc.) for the Oklahoma communities that wereimpacted by the ice storm event during the pandemic.

Translating Social Media Crisis Narratives into Road Network Utilization Metrics: The Case of 2020 Oklahoma Ice Storm