Risk-Prone and Risk-Averse Behavior in Natural Emergencies: An Appraisal Theory Approach

2025-03-27

AI Summary

This study investigates individual risk-behavior tendencies—risk-seeking versus risk-averse—in natural disasters and their modulation by affective cognition and social influence. Grounded in appraisal theory, it integrates vector-based geographic trajectories and textual content from 774 Twitter users during Hurricane Sandy to construct a multi-source spatiotemporal risk exposure assessment framework, incorporating evacuation plans, ground-truth flood measurements, and explicit/implicit geotags. Methodologically, it pioneers the integration of appraisal theory with vector trajectory modeling to quantify risk propensity, and proposes a novel analytical paradigm combining geospatial information mining, emotion- and action-oriented text classification, and multi-source spatial data fusion. Results indicate that users sharing actionable information exhibit marginally higher risk exposure; the population overall demonstrates risk-averse migration behavior; larger social neighborhood size reduces exposure, whereas neighborhoods with high tweet frequency increase exposure probability.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Application Domains: Humanities & Computational Social Science
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

The paper examines social media content to measure and model risk behavior in natural emergencies from an appraisal theory perspective. We calculate individual risk behavior quotients and relate them to individual and peer emotional and actionable cognitive responses for 774 individual Twitter users affected by the Sandy hurricane landfall. We employ vector analysis to compute risk behavior quotients. By utilizing geographic information associated with the tweets, both implicitly and explicitly, we track each user's path and determine the average vector of their movement. The risk quotient is obtained by comparing risk exposure at the origin and destination of the average vector. We assess risk exposure for each zone in the study area by combining pre-hurricane evacuation plans with post-event flooding data, as reported by the National Weather Service. By using the emotional and actionable content of the tweets as predictors for risk, we found that sharing actionable information relates to slightly higher risk exposure. At the same time, overall, the subjects tended to move away from the riskiest areas of the storm. Finally, individuals surrounded by more peers are less likely to be affected, while those surrounded by more tweeting activity are more likely to be affected risk-prone.

Problem

• Measure risk behavior in natural emergencies using social media
• Relate risk behavior to emotional and actionable Twitter responses
• Analyze peer influence on risk-prone behavior during hurricanes

Innovation

• Calculate risk behavior quotients via vector analysis
• Track user paths using geographic tweet data
• Predict risk with emotional and actionable tweet content