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A novel surveillance approach for disaster mental health
Disasters have substantial consequences for population mental health. Social media data present an opportunity for mental health surveillance after disasters to help identify areas of mental health needs. We aimed to 1) identify specific basic emotions from Twitter for the greater New York City area during Hurricane Sandy, which made landfall on October 29, 2012, and to 2) detect and map spatial temporal clusters representing excess risk of these emotions.
We applied an advanced sentiment analysis on 344,957 Twitter tweets in the study area over eleven days, from October 22 to November 1, 2012, to extract basic emotions, a space-time scan statistic (SaTScan) and a geographic information system (QGIS) to detect and map excess risk of these emotions.
Sadness and disgust were among the most prominent emotions identified. Furthermore, we noted 24 spatial clusters of excess risk of basic emotions over time: Four for anger, one for confusion, three for disgust, five for fear, five for sadness, and six for surprise. Of these, anger, confusion, disgust and fear clusters appeared pre disaster, a cluster of surprise was found peri disaster, and a cluster of sadness emerged post disaster.
We proposed a novel syndromic surveillance approach for mental health based on social media data that may support conventional approaches by providing useful additional information in the context of disaster. We showed that excess risk of multiple basic emotions could be mapped in space and time as a step towards anticipating acute stress in the population and identifying community mental health need rapidly and efficiently in the aftermath of disaster. More studies are needed to better control for bias, identify associations with reliable and valid instruments measuring mental health, and to explore computational methods for continued model-fitting, causal relationships, and ongoing evaluation. Our study may be a starting point also for more fully elaborated models that can either prospectively detect mental health risk using real-time social media data or detect excess risk of emotional reactions in areas that lack efficient infrastructure during and after disasters. As such, social media data may be used for mental health surveillance after large scale disasters to help identify areas of mental health needs and to guide us in our knowledge where we may most effectively intervene to reduce the mental health consequences of disasters.
The authors obtained the raw dataset from Harvard Center for Geographical Analysis (CGA, http://www.gis.harvard.edu), the institution that collected the data. The authors did not have a role in the data collection. Since the raw dataset contains detailed information of individuals at a high spatio-temporal resolution, and since the data is owned solely by Twitter users (see Twitter's data sharing policy), the authors consider the raw dataset to be third-party data, which unfortunately cannot be made publicly available. The authors had access to the raw tweet data from CGA because they are researchers at Harvard and formally agreed not to share the raw data publicly. However, the authors appreciate transparency in research and are ready to make the Tweet IDs used in the current study available, according to Twitter's sharing policy. With information of the Tweet IDs, researchers will be able to look up those IDs using the Twitter API to get full tweets for verifying the conclusions made in the paper. In addition, the authors can make the raw data available to researchers who have a formal affiliation with Harvard University. Harvard also provides a rehydration app for independent researchers to facilitate conversions of TweetIDs back to full tweets, https://github.com/cga-harvard/hhypermap-bop/tree/master/BOP-utilities/T.... Please direct inquiries for IDs or the raw dataset to Wendy Guan (firstname.lastname@example.org) or Benjamin Lewis (email@example.com) of CGA, or to the corresponding author (firstname.lastname@example.org).