Using Social Media to Detect Socio-Economic Disaster Recovery

Abstract

There has been growing interest in harnessing Artificial Intelligence (AI) to improve situational awareness for disaster management. However, to the authors’ best knowledge, few studies have focused on socio-economic recovery. Here, as a first step toward investigating the possibility of developing an AI-based method for detecting socio-economic recovery, this study provides fundamental insights about the correlations between public sentiment on social media and socio-economic recovery activities as reflected in market data. Our result shows multiple correlations between sentiment on social media and the socio-economic recovery activities involved in restarting daily routines. Conventional socio-economic recovery indicators, such as governmental statistical data, have a significant time lag before publishing. Therefore, by taking advantages of the real timeliness and the effectiveness of seizing communication trends of massive social media data, using public sentiment on social media can improve situational awareness in recovery operations.