Spatio-Temporal Distribution of Negative Emotions in New York City After a Natural Disaster as Seen in Social Media

Abstract

Disasters have substantial consequences for population mental health. We used Twitter to (1) extract negative emotions indicating discomfort in New York City (NYC) before, during, and after Superstorm Sandy in 2012. We further aimed to (2) identify whether pre- or peri-disaster discomfort were associated with peri- or post-disaster discomfort, respectively, and to (3) assess geographic variation in discomfort across NYC census tracts over time. Our sample consisted of 1,018,140 geo-located tweets that were analyzed with an advanced sentiment analysis called ”Extracting the Meaning Of Terse Information in a Visualization of Emotion” (EMOTIVE). We calculated discomfort rates for 2137 NYC census tracts, applied spatial regimes regression to find associations of discomfort, and used Moran’s I for spatial cluster detection across NYC boroughs over time. We found increased discomfort, that is, bundled negative emotions after the storm as compared to during the storm. Furthermore, pre- and peri-disaster discomfort was positively associated with post-disaster discomfort; however, this association was different across boroughs, with significant associations only in Manhattan, the Bronx, and Queens. In addition, rates were most prominently spatially clustered in Staten Island lasting pre- to post-disaster. This is the first study that determined significant associations of negative emotional responses found in social media posts over space and time in the context of a natural disaster, which may guide us in identifying those areas and populations mostly in need for care.

Factors Associated with Food Insecurity Following Hurricane Harvey in Texas

“…Disasters cause pervasive disruption across all levels of the socio-ecological model. Individuals experience stress potentially due to witnessing the disaster, sheltering or evacuating, displacement, or disruption to normal routines [17][18][19][20][21]. Households may experience damage to their home, stress on familial relationships when coping with disaster impacts, or changes in household material or financial resources as a result of disaster exposure [22][23][24].…”

A Systematic Review of Techniques Employed for Determining Mental Health Using Social Media in Psychological Surveillance During Disasters

“…We extracted the data from the 18 articles identified for the review. Table 1 shows the summary of the included articles, [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45] and Appendix 2 gives the data extracted from the included studies in an Excel sheet. Appendix 3 shows the list of studies that were excluded after the full text of the articles was completely read.…”

“…Appendix 4 summarizes the number of studies based on their characteristics. Among the studies, 9 studies are from USA, 30,31,33,35,[38][39][40]42,45 2 each from France 37,41 and Japan, 29,43 and 1 each from Germany, 36 Mexico, 32 South Korea, 34 and the Netherlands. 44 Except 1 study, 28 all the other reviewed studies used data from Twitter, a widely used social media platform, for analysis.…”

“…Khalid et al 28 collected personal narratives and stories from blogs for emotional analysis. The maximum number of included studies 29,30,35,37,38,41,42,44,45 examined negative emotions (sad, anger, fear, confusion, disgust, shame) from the social media texts as an indicator of mental health problems. Three studies 33,40,43 observed sentiments (positive, negative, and neutral) from the social media texts to analyze the psychological state of the population.…”

Research Frontiers of Health Emergency and Disaster Risk Management: What Do We Know So Far?

“…Using the computerized random digit dialling methods for rapid data collection after a major urban subway fire incident, Chan, Huang, Hung et al [8] captured health risk perception, misconceptions, and community first-aid response knowledge in urban man-made emergency incidents. Using social media data from Twitter, Gruebner, Lowe, Sykora et al [9] showed how the spatio-temporal distribution of negative emotions varied in New York City after a natural disaster. Their study showed that pre-disaster status could be used as a significant predictor of post-disaster emotional outcomes in communities.…”