Semiautomated social media analytics for sensing societal impacts due to community disruptions during disasters

Abstract

Understanding the societal impacts caused by community disruptions (e.g., power outages and road closures), particularly during the response stage, with timeliness and sufficient detail is an underexplored, yet important, consideration. It is critical for effective decision-making and coordination in disaster response and relief activities as well as post-disaster virtual reconnaissance activities. This study proposes a semiautomated social media analytics approach for social sensing of Disaster Impacts and Societal Considerations (SocialDISC). This approach addresses two limitations of existing social media analytics approaches: lacking adaptability to the need of different analyzers or different disasters and missing the information related to subjective feelings, emotions, and opinions of the people. SocialDISC labels and clusters social media posts in each disruption category to facilitate scanning by analyzers. Analyzers, in this paper, are persons who acquire social impact information from social media data (e.g., infrastructure management personnel, volunteers, researchers from academia, and some residents impacted by the disaster). Furthermore, SocialDISC enables analyzers to quickly parse topics and emotion signals of each subevent to assess the societal impacts caused by disruption events. To demonstrate the performance of SocialDISC, the authors proposed a case study based on Hurricane Harvey, one of the costliest disasters in U.S. history, and analyzed the disruptions and corresponding societal impacts in different aspects. The analysis result shows that Houstonians suffered greatly from flooded houses, lack of access to food and water, and power outages. SocialDISC can foster an understanding of the relationship between disruptions of infrastructures and societal impacts, expectations of the public when facing disasters, and infrastructure interdependency and cascading failures. SocialD-ISC's provision of timely information about the societal impacts of people may help disaster response decision-making. 1 INTRODUCTION Disaster-induced community disruptions include physical infrastructure and social disruptions, such as hazards, lack of supplies, business interruption losses, and evacuation orders

Towards Fairness-Aware Disaster Informatics: an Interdisciplinary Perspective

“…Similar approaches also exist in identifying disaster-related images [10]. Sentiment analysis discerns the polarity of sentiments and emotion signals as indicators of disaster impacts on public well-being [11]. Second, for information integration approaches, state-of-theart natural language processing approaches support the detection and characterization of disaster situations and subevents by integrating social media posts with similar contents [12].…”

Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data

“…Another limitation is the dearth of empirical studies measuring community resilience based on data capturing the complex interactions of the nexus of populations, businesses and the built environment. Most existing studies have relied on two data types: surveys [23,[28][29][30][31][32] and social media data [33][34][35][36][37][38]. Surveys present two drawbacks for collection of community resilience data.…”

Adapting Social Impact Assessment to Flood Risk Management

“…• Concept of social impact: the topics most often discussed in the analyses applied to social impacts and disasters were: deaths [87,88], effects on health [89,90]; infrastructure service destruction [91,92]; disruption of normal life [93,94]; and damages to economic activities [95,96]. Other studies, with lower frequency, encompassed additional areas of social impact with greater depth, which included intangible factors such as psycho-social effects [97,98], trust towards institutions [99,100] and flood risk awareness [101,102].…”

“…The dislocation of everyday socio-economic circuits, together with the costs of repair and rehabilitation, may result in a loss and/or deterioration of goods and means of subsistence, which may then cause a fall in purchasing power, not only among directly affected individuals, but also among those belonging to the adjacent or secondarily affected areas that have experienced less or zero flooding. Various analysts [93,102,103] have recommended semi-automated social media analytics for rapidly estimating direct flood damages and obtaining a basis for assessing other impacts.…”