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Karmegam and Mappillairaju Int J Health Geogr (2020) 19:19 International Journal of https://doi.org/10.1186/s12942-020-00214-4 Health Geographics RESEARCH Open Access Spatio-temporal distribution of negative emotions on Twitter during foods in Chennai, India, in 2015: a post hoc analysis Dhivya Karmegam1* and Bagavandas Mappillairaju2 Abstract 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 afected 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 fooding, which subsequently triggered eco- nomic 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 foods; (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 foods were aggregated for detailed categorisation and analysis. The diferent emotions were extracted and classifed by using the National Research Council emotion lexicon. Both an analysis of variance (ANOVA) and mixed-efect 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 foods, negative emotions were in evidence 17.02% before, 29.45% during and 11.39% after the foods. The rates of negative emotions showed signifcant 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 identifed. 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 pro- vides insight into early identifcation of mental health issues, and their consequences, for those afected by disasters. Keywords: Emotional analysis, Spatial statistics, Disaster mental health, Geographic information system, Twitter Introduction Te world has witnessed a rise in natural disasters such as foods, hurricanes and storms due to the efects of climate change [1]. During many of these events, populations *Correspondence: [email protected] afected by natural disasters are susceptible to a wide 1 School of Public Health, SRM Institute of Science and Technology, Chennai, Tamil Nadu 603203, India range of mental health problems [2, 3]. Consequently, a Full list of author information is available at the end of the article number of people are found to experience post-traumatic © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/publi cdoma in/ zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Karmegam and Mappillairaju Int J Health Geogr (2020) 19:19 Page 2 of 13 stress disorders, anxiety, depressive disorder, and dis- the psychological stress that they encounter after the dis- tress as a direct result of living through the event [4–7]. aster. A spatio-temporal analysis to identify the clusters Identifying the psychological consequences of disasters in which people display negative emotions, which could is a challenge for researchers, and traditional data col- quickly deteriorate into more severe psychological prob- lection methods such as questionnaires, interviews, and lems, thus becomes important. Similarly, spatio-temporal focus groups discussions may introduce delays and a time analysis of clusters during a disease outbreak has helped lag. Further, timely capture of data immediately before, in the identifcation of hotspots, areas of outbreak and during, and after a disaster may be impossible to accom- the population at risk [37–39]. Tis data has helped the plish and is likely to be lost [8]. During disasters, people concerned management teams to undertake emergency are known to convey messages for help and express their public health interventions [40, 41]. If data on negative feelings through social media. Facebook status updates, emotions of people can be gathered along with their time tweets, and YouTube videos posted during the disaster stamps, in addition to their geographic location, it pro- provide an insight into the prevailing mood of people in vides an insight into the spatial clusters that experience specifc locations [9–11]. During the past decade, social extreme negative emotions and the specifc time of such media has been employed by disaster management teams occurrence. To the best of our knowledge, no study has to study situational awareness [12–14], plan response undertaken a spatio-temporal analysis of negative emo- [15], and undertake relief activities [16–18]. Social media tions using data from social media in India [42]. Using updates during disasters provide real-time data that is geo-coded Twitter messages posted around the time of not possible to be collected using traditional data collec- foods during November/December 2015 in Chennai, the tion methods. In particular, real-time data can be tapped southern Indian city and capital of Tamil Nadu state, we efectively to identify the mental health status of individ- undertook a spatio-temporal study of negative emotions uals or an entire population [19–22]. Twitter has proved expressed by people in the afected areas. Te aim of our efective for such an analysis. Using emotional analysis, study was three-fold: (i) quantify emotions (extracted negative emotions expressed in tweets can be classifed from Twitter feed) expressed by the people of Chennai into fear, anger or sadness and tied to diferent disaster before, during and after the 2015 foods; (ii) understand time periods [23, 24]. Further, a spatio-temporal analysis the spatial and temporal variations of negative motions of tweets can be done to identify the spatial clusters that across Chennai city via tweets at the ward level; and (iii) display negative emotions at specifc points in time [25, compare the negative emotion rates before and during 26]. Negative emotions are particularly important as they the foods with those during and after the foods. have been shown to result in adverse mental health con- sequences in individuals, especially in a disaster context Methods [2, 27]. Te objective of this study was to gain an understanding Studies have identifed people experiencing psychologi- of the spatial and temporal variations of negative emo- cal consequences of traumatic events from their social tions across Chennai during an unexpected disaster. To media updates by extracting emotions and sentiments ascertain the temporal variations in negative emotion expressed by them from those updates [23, 28–32]. Some rates, tweets posted by people in Chennai before, during of the studies have also investigated the geographic vari- and after the foods that ravaged the city in November/ ations in emotions [25, 26, 33]. Te identifcation of emo- December 2015 were used in our study. tions of the distressed population by location provides an insight into the stress experienced by people in specifc Study setting locations. Analysis of the general mood and alertness Te Corporation of Chennai has divided the city’s metro- levels of a population cluster-wise also becomes possi- politan area into 15 zones. Tese zones are further sub- ble [34]. Te emotional status of people in a spatial clus- divided into 200 wards for administrative convenience. ter can be further drilled down to specifc time periods Figure 1 shows a map of Chennai that identifes all the using a temporal analysis. Once real-time spatio-tempo- zones and wards. Before discussing the study method, ral information on specifc clusters experiencing severe it would be pertinent to describe the dire situation the stress becomes available, public health authorities can city was put in due to the foods in November/December make early interventions to arrest further damage and, 2015. where required, also provide appropriate mental health Due to its fat topography, Chennai already faces the interventions [35, 36]. problem of poor drainage capacity in some areas. Te Te psychological status of people before a disaster acts city has been experiencing severe foods once in a decade as a predictor of psychological problems they experience from the 1970s. Heavy rainfall and the resultant food- during a disaster [27], which in turn gives an idea about ing threw normal life out of gear and afected the city’s Karmegam and Mappillairaju Int J Health Geogr (2020) 19:19 Page 3 of 13 Fig.

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