Natural language processing for disaster-resilient infrastructure: Research focus and future opportunities

Abstract

The increasing frequency and severity of natural disasters, exacerbated by global warming, necessitate novel solutions to strengthen the resilience of Critical Infrastructure Systems (CISs). Recent research reveals the significant potential of natural language processing (NLP) to analyze unstructured human language during disasters, thereby facilitating the uncovering of disruptions and providing situational awareness supporting various aspects of resilience regarding CISs. Despite this potential, few studies have systematically mapped the global research on NLP applications with respect to supporting various aspects of resilience of CISs. This paper contributes to the body of knowledge by presenting a review of current knowledge using the scientometric review technique. Using 231 bibliographic records from the Scopus and Web of Science core collections, we identify five key research areas where researchers have used NLP to support the resilience of CISs during natural disasters, including sentiment analysis, crisis informatics, data and knowledge visualization, disaster impacts, and content analysis. Furthermore, we map the utility of NLP in the identified research focus with respect to four aspects of resilience (i.e., preparedness, absorption, recovery, and adaptability) and present various common techniques used and potential future research directions. This review highlights that NLP has the potential to become a supplementary data source to support the resilience of CISs. The results of this study serve as an introductory-level guide designed to help scholars and practitioners unlock the potential of NLP for strengthening the resilience of CISs against natural disasters.

Keywords

Natural language processing / NLP / Critical infrastructure / Resilience / Disaster

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