A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data. (10th June 2020)

• Record Type: Journal Article
• Authors: Roy, Kamol Chandra; Hasan, Samiul; Mozumder, Pallab
• Is Part Of: Computer-aided civil and infrastructure engineering. Volume 35:Number 12(2020:Dec.)
• Page Start: 1387
• Page End: 1402
• Publication Date: 2020-06-10
• Subjects: Civil engineering -- Data processing -- Periodicals; Computer-aided engineering -- Periodicals; 624.0285
• DOI: 10.1111/mice.12573
• Languages: English
• ISSNs: 1093-9687

Abstract

Abstract: Rapid identification of infrastructure disruptions during a disaster plays an important role in restoration and recovery operations. Due to the limitations of using physical sensing technologies, such as the requirement to cover a large area in a short period of time, studies have investigated the potential of social sensing for damage/disruption assessment following a disaster. However, previous studies focused on identifying whether a social media post is damage related or not. Hence, advanced methods are needed to infer actual infrastructure disruptions and their locations from such data. In this paper, we present a multilabel classification approach to identify the co‐occurrence of multiple types of infrastructure disruptions considering the sentiment toward a disruption—whether a post is reporting an actual disruption (negative), or a disruption in general (neutral), or not affected by a disruption (positive). In addition, we propose a dynamic mapping framework for visualizing infrastructure disruptions. We use a geo‐parsing method that extracts location from the texts of a social media post. We test the proposed approach using Twitter data collected during hurricanes Irma and Michael. The proposed multilabel classification approach performs better than a baseline method (using simple keyword search and sentiment analysis). We also find that disruption‐related tweets, based on specific keywords, do not necessarily indicate an actual disruption. Many tweets represent general conversations, concerns about a potential disruption, and positive emotion for not being affected by any disruption. In addition, a dynamic disruption map has potential in showing county and point/coordinate level disruptions. Identifying disruption types and their locations is vital for disaster recovery, response, and relief actions. By inferring the co‐occurrence of multiple disruptions, the proposed approach may help coordinate among infrastructure service providers and disaster management organizations.