Spatio-temporal distribution of negative emotions on Twitter during floods in Chennai, India, in 2015: a post hoc analysis.

Background: Natural disasters are known to take their psychological toll immediately, and over the long term, on those living through them. Messages posted on Twitter provide an insight into the state of mind of citizens affected by such disasters and provide useful data on the emotional impact on groups of people. In 2015, Chennai, the capital city of Tamil Nadu state in southern India, experienced unprecedented flooding, which subsequently triggered economic losses and had considerable psychological impact on citizens. The objectives of this study are to (i) mine posts to Twitter to extract negative emotions of those posting tweets before, during and after the floods; (ii) examine the spatial and temporal variations of negative emotions across Chennai city via tweets; and (iii) analyse associations in the posts between the emotions observed before, during and after the disaster.

Methods: Using Twitter's application programming interface, tweets posted at the time of floods were aggregated for detailed categorisation and analysis. The different emotions were extracted and classified by using the National Research Council emotion lexicon. Both an analysis of variance (ANOVA) and mixed-effect analysis were performed to assess the temporal variations in negative emotion rates. Global and local Moran's I statistic were used to understand the spatial distribution and clusters of negative emotions across the Chennai region. Spatial regression was used to analyse over time the association in negative emotion rates from the tweets.

Results: In the 5696 tweets analysed around the time of the floods, negative emotions were in evidence 17.02% before, 29.45% during and 11.39% after the floods. The rates of negative emotions showed significant variation between tweets sent before, during and after the disaster. Negative emotions were highest at the time of disaster's peak and reduced considerably post disaster in all wards of Chennai. Spatial clusters of wards with high negative emotion rates were identified.

Conclusions: Spatial analysis of emotions expressed on Twitter during disasters helps to identify geographic areas with high negative emotions and areas needing immediate emotional support. Analysing emotions temporally provides insight into early identification of mental health issues, and their consequences, for those affected by disasters.