Emotional Health and Climate-Change-Related Stressor Extraction from Social Media: A Case Study Using Hurricane Harvey

Thanh Bui, Andrea Hannah, Sanjay Madria, Rosemary Nabaweesi, Eugene Levin, Michael Wilson and Long Nguyen ()
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Thanh Bui: Department of Electrical Engineering and Computer Science, University of Arkansas, Fayetteville, AR 72701, USA
Andrea Hannah: School of Applied Computational Sciences, Meharry Medical College, Nashville, TN 37203, USA
Sanjay Madria: Department of Computer Science, Missouri University of Science and Technology, Rolla, MO 65409, USA
Rosemary Nabaweesi: Center for Health Policy, Department of Public Health Practice, Meharry Medical College, Nashville, TN 37208, USA
Eugene Levin: School of Applied Computational Sciences, Meharry Medical College, Nashville, TN 37203, USA
Michael Wilson: APSU GIS Center, Austin Peay State University, Clarksville, TN 37040, USA
Long Nguyen: School of Applied Computational Sciences, Meharry Medical College, Nashville, TN 37203, USA

Mathematics, 2023, vol. 11, issue 24, 1-16

Abstract: Climate change has led to a variety of disasters that have caused damage to infrastructure and the economy with societal impacts to human living. Understanding people’s emotions and stressors during disaster times will enable preparation strategies for mitigating further consequences. In this paper, we mine emotions and stressors encountered by people and shared on Twitter during Hurricane Harvey in 2017 as a showcase. In this work, we acquired a dataset of tweets from Twitter on Hurricane Harvey from 20 August 2017 to 30 August 2017. The dataset consists of around 400,000 tweets and is available on Kaggle. Next, a BERT-based model is employed to predict emotions associated with tweets posted by users. Then, natural language processing (NLP) techniques are utilized on negative-emotion tweets to explore the trends and prevalence of the topics discussed during the disaster event. Using Latent Dirichlet Allocation (LDA) topic modeling, we identified themes, enabling us to manually extract stressors termed as climate-change-related stressors. Results show that 20 climate-change-related stressors were extracted and that emotions peaked during the deadliest phase of the disaster. This indicates that tracking emotions may be a useful approach for studying environmentally determined well-being outcomes in light of understanding climate change impacts.

Keywords: emotional health; stressors; topic modeling; climate change; social media (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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