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

Author

Listed:
  • Thanh Bui

    (Department of Electrical Engineering and Computer Science, University of Arkansas, Fayetteville, AR 72701, USA
    These authors contributed equally to this work.)

  • Andrea Hannah

    (School of Applied Computational Sciences, Meharry Medical College, Nashville, TN 37203, USA
    These authors contributed equally to this work.)

  • 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)

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.

Suggested Citation

  • Thanh Bui & Andrea Hannah & Sanjay Madria & Rosemary Nabaweesi & Eugene Levin & Michael Wilson & Long Nguyen, 2023. "Emotional Health and Climate-Change-Related Stressor Extraction from Social Media: A Case Study Using Hurricane Harvey," Mathematics, MDPI, vol. 11(24), pages 1-16, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:24:p:4910-:d:1297039
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    References listed on IDEAS

    as
    1. Quyen G. To & Kien G. To & Van-Anh N. Huynh & Nhung T. Q. Nguyen & Diep T. N. Ngo & Stephanie J. Alley & Anh N. Q. Tran & Anh N. P. Tran & Ngan T. T. Pham & Thanh X. Bui & Corneel Vandelanotte, 2021. "Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(8), pages 1-9, April.
    2. Jianping Zhu & Futian Weng & Muni Zhuang & Xin Lu & Xu Tan & Songjie Lin & Ruoyi Zhang, 2022. "Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model," IJERPH, MDPI, vol. 19(20), pages 1-26, October.
    Full references (including those not matched with items on IDEAS)

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