Twitter Use in Hurricane Isaac and Its Implications for Disaster Resilience

Abstract

Disaster resilience is the capacity of a community to “bounce back” from disastrous events. Most studies rely on traditional data such as census data to study community resilience. With increasing use of social media, new data sources such as Twitter could be utilized to monitor human response during different phases of disasters to better understand resilience. An important research question is: Does Twitter use correlate with disaster resilience? Specifically, will communities with more disaster-related Twitter uses be more resilient to disasters, presumably because they have better situational awareness? The underlying issue is that if there are social and geographical disparities in Twitter use, how will such disparities affect communities’ resilience to disasters? This study examines the relationship between Twitter use and community resilience during Hurricane Isaac, which hit Louisiana and Mississippi in August 2012. First, we applied the resilience inference measurement (RIM) model to calculate the resilience indices of 146 affected counties. Second, we analyzed Twitter use and their sentiment patterns through the three phases of Hurricane Isaac—preparedness, response, and recovery. Third, we correlated Twitter use density and sentiment scores with the resilience scores and major social–environmental variables to test whether significant geographical and social disparities in Twitter use existed through the three phases of disaster management. Significant positive correlations were found between Twitter use density and resilience indicators, confirming that communities with higher resilience capacity, which are characterized by better social–environmental conditions, tend to have higher Twitter use. These results imply that Twitter use during disasters could be improved to increase the resilience of affected communities. On the other hand, no significant correlations were found between sentiment scores and resilience indicators, suggesting that further research on sentiment analysis may be needed.

Understanding Human Activities in Response to Typhoon Hato from Multi-Source Geospatial Big Data: A Case Study in Guangdong, China

“…Many theoretical studies on urban resilience to disasters have been completed [41,54,55] and existing studies show that the urban recovery time after a natural hazard is associated with socioeconomic disparities [26,33,56]. In this study, we further explored the urban resilience from the NLR anomalies by examining the response and recovery time during Hato.…”

“…The recovery stage refers to the process over which human activities gradually bounce back normal and it is widely used to study a city's resilience [26,33,54]. In this study, we calculated the recovery time to evaluate how fast a city returns to normal after the typhoon.…”

“…Over recent years, the use of mobile phones and social media platforms has provided new insights on disaster management. The social sensing data [9], such as mobile phone records and social media posts, can record public behaviors in the physical and cyber space at high spatial and temporal resolutions and have been widely used in typhoon-related studies, in particularly in disaster-related information extraction [10][11][12][13][14][15], situation awareness [16][17][18][19][20], resilience assessment [21][22][23][24][25][26], and rapid damage assessment [27][28][29][30][31][32].…”

Temporal, Spatial, and Socioeconomic Dynamics in Social Media Thematic Emphases during Typhoon Mangkhut

“…Significant associations between social media use and socioeconomic factors, households in rural areas, lower-income groups, and racial minorities are more likely to report greater inaccuracies in social media information [39]. Significant positive correlations are found between Twitter use density and resilience indicators, confirming that communities with higher resilience capacity or those characterized by better social-environmental conditions tend to have higher Twitter use [40].…”

Actionable Information in Flood Risk Communications and the Potential for New Web-Based Tools for Long-Term Planning for Individuals and Community

“…Niles et al (2019) reported that use of Twitter varies by the various stages of disaster and by the type of disaster, with use in hurricane events peaking at the preparation stage and in flood and tornado events at the "during" or recovery stage. Wang et al (2021) found that Twitter use has a positive correlation with hurricane resiliency, suggesting that it can be a tool to enhance resilience. Machine learning techniques have revealed the temporal evolution of the various "stages" of Tweet applications in the context of a single disaster (Arapostathis, 2021), with other research calling for further integration of machine-learning-derived information in this regard (Dwarakanath et al, 2021).…”