Exploring Cognitive Sustainability Concerns in Public Responses to Extreme Weather Events: An NLP Analysis of Twitter Data

Main Article Content

Riheme Berbère
Safa Elkefi
Achraf Tounsi


The United States has a long history of experiencing extreme weather events. Hurricanes are among the most devastating natural disasters that have significant economic and physical impacts on the country. By applying Natural Language Processing (NLP) to Twitter data for sentiment analysis, emotion detection, and topic modelling, this study provides a more thorough understanding of public response and concerns during five study cases of hurricanes that hit the United States: Harvey, Irma, Maria, Ida, and Ian. The findings on sentiment analysis revealed that 64.75% of the tweets were classified as Negative and 35.25% as Positive. For emotion detection, the predominant emotion was anger, with 39.91%. These results were centred around the main public concerns shown by the topic modelling: hurricane management, donation and support, and disaster impacts. Our future work will focus on understanding people’s responses to extreme weather events through the evolving concept of Cognitive Sustainability.

Article Details

How to Cite
Berbère, R., Elkefi, S., LAYEB, S. B., & Tounsi, A. . (2023). Exploring Cognitive Sustainability Concerns in Public Responses to Extreme Weather Events: An NLP Analysis of Twitter Data. Cognitive Sustainability, 2(4). https://doi.org/10.55343/cogsust.80


Abel, F., Hauff, C., Houben, G. J., Stronkman, R., & Tao, K. (2012, June). Semantics+ filtering+ search= twitcident. exploring information in social web streams. In Proceedings of the 23rd ACM conference on Hypertext and social media (pp. 285-294). DOI: https://doi.org/gftcp3

Acheampong, F. A., Nunoo-Mensah, H., & Chen, W. (2021). Transformer models for text-based emotion detection: a review of BERT-based approaches. Artificial Intelligence Review, 1-41. DOI: https://doi.org/gmxnr9

Albahli, S., Algsham, A., Aeraj, S., Alsaeed, M., Alrashed, M., Rauf, H. T., ... & Mohammed, M. A. (2021). COVID-19 Public Sentiment Insights: A Text Mining Approach to the Gulf Countries. Computers, Materials & Continua, 67(2). DOI: https://doi.org/k9bj

Al-Garadi, M. A., Yang, Y. C., & Sarker, A. (2022, November). The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges. In Healthcare (Vol. 10, No. 11, p. 2270). MDPI. DOI: https://doi.org/k9bh

Barbieri, F., Camacho-Collados, J., Neves, L., & Espinosa-Anke, L. (2020). Tweeteval: Unified benchmark and comparative evaluation for tweet classification. arXiv preprint arXiv:2010.12421. DOI: https://doi.org/k9bp

Catelli, R., Pelosi, S., & Esposito, M. (2022). Lexicon-based vs. Bert-based sentiment analysis: A comparative study in Italian. Electronics, 11(3), 374. DOI: https://doi.org/k9bn

Dash, N., Morrow, B. H., Mainster, J., & Cunningham, L. (2007). Lasting effects of Hurricane Andrew on a working-class community. Natural Hazards Review, 8(1), 13-21. DOI: https://doi.org/fqpbsn

Ebi, K. L., & Bowen, K. (2016). Extreme events as sources of health vulnerability: Drought as an example. Weather and climate extremes, 11, 95-102. DOI: https://doi.org/gg3r27

Ebi, K. L., Vanos, J., Baldwin, J. W., Bell, J. E., Hondula, D. M., Errett, N. A., ... & Berry, P. (2021). Extreme weather and climate change: population health and health system implications. Annual review of public health, 42(1), 293-315. DOI: https://doi.org/gnhm97

Fischer, E. M., & Knutti, R. (2015). Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nature climate change, 5(6), 560-564. DOI: https://doi.org/35h

Karami, A., Shah, V., Vaezi, R., & Bansal, A. (2020). Twitter speaks: A case of national disaster situational awareness. Journal of Information Science, 46(3), 313-324. DOI: https://doi.org/ggwkc4

Kwasinski, A., Andrade, F., Castro-Sitiriche, M. J., & O’Neill-Carrillo, E. (2019). Hurricane Maria effects on Puerto Rico electric power infrastructure. IEEE Power and Energy Technology Systems Journal, 6(1), 85-94. DOI: https://doi.org/gp497r

Lu, Y., & Yang, D. (2011). Information exchange in virtual communities under extreme disaster conditions. Decision Support Systems, 50(2), 529-538. DOI: https://doi.org/fnnh27

Mills, M. A., Edmondson, D., & Park, C. L. (2007). Trauma and stress response among Hurricane Katrina evacuees. American journal of public health, 97(Supplement_1), S116-S123. DOI: https://doi.org/brsvmf

Mitsova, D., Escaleras, M., Sapat, A., Esnard, A. M., & Lamadrid, A. J. (2019). The effects of infrastructure service disruptions and socio-economic vulnerability on hurricane recovery. Sustainability, 11(2), 516. DOI: https://doi.org/10.3390/su11020516

Neppalli, V. K., Caragea, C., Squicciarini, A., Tapia, A., & Stehle, S. (2017). Sentiment analysis during Hurricane Sandy in emergency response. International journal of disaster risk reduction, 21, 213-222. DOI: https://doi.org/gbj94q

Nguyen, Q. T., Nguyen, T. L., Luong, N. H., & Ngo, Q. H. (2020, November). Fine-tuning bert for sentiment analysis of vietnamese reviews. In 2020 7th NAFOSTED conference on information and computer science (NICS) (pp. 302-307). IEEE. DOI: https://doi.org/k9bm

NOAA National Centers for Environmental Information (NCEI) U.S. Billion-Dollar Weather and Climate Disasters (2023). https://www.ncei.noaa.gov/access/billions/, DOI: https://doi.org/gmhcnv

Oluyomi, A. O., Panthagani, K., Sotelo, J., Gu, X., Armstrong, G., Luo, D. N., ... & Bondy, M. (2021). Houston hurricane Harvey health (Houston-3H) study: assessment of allergic symptoms and stress after hurricane Harvey flooding. Environmental Health, 20(1), 1-15. DOI: https://doi.org/gjxrb8

Shultz, J. M., Trapido, E. J., Kossin, J. P., Fugate, C., Nogueira, L., Apro, A., ... & Galea, S. (2022). Hurricane Ida's impact on Louisiana and Mississippi during the COVID-19 delta surge: Complex and compounding threats to Population Health. The Lancet Regional Health–Americas, 12. DOI: https://doi.org/k9br

Stephenson, D. B., Diaz, H. F., & Murnane, R. J. (2008). Definition, diagnosis, and origin of extreme weather and climate events. Climate extremes and society, 340, 11-23. URL: https://empslocal.ex.ac.uk/people/staff/dbs202/publications/2008/extremes.pdf

Sun, S., Kim, J. H., Jung, H. S., Kim, M., Zhao, X., & Kamphuis, P. (2023). Exploring Hype in Metaverse: Topic Modeling Analysis of Korean Twitter User Data. Systems, 11(3), 164. DOI: https://doi.org/k9bq

Sutton, J., & Tierney, K. (2006). Disaster preparedness: Concepts, guidance, and research. Colorado: University of Colorado, 3(1). URL: https://www.bencana-kesehatan.net/arsip/images/referensi/april/Disaster%20Preparedness%20Concepts_Jurnal.pdf

Vieweg, S., Hughes, A. L., Starbird, K., & Palen, L. (2010, April). Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1079-1088). DOI: https://doi.org/fjck8p

Waddell, S. L., Jayaweera, D. T., Mirsaeidi, M., Beier, J. C., & Kumar, N. (2021). Perspectives on the health effects of hurricanes: a review and challenges. International journal of environmental research and public health, 18(5), 2756. DOI: https://doi.org/gr934k

Yuan, F., Li, M., & Liu, R. (2020). Understanding the evolutions of public responses using social media: Hurricane Matthew case study. International journal of disaster risk reduction, 51, 101798. DOI: https://doi.org/k9bg

Zhou, S., Kan, P., Huang, Q., & Silbernagel, J. (2023). A guided latent Dirichlet allocation approach to investigate real-time latent topics of Twitter data during Hurricane Laura. Journal of Information Science, 49(2), 465-479. DOI: https://doi.org/k9bk

Zoldy, M., Csete, M. S., Kolozsi, P. P., Bordas, P., & Torok, A. (2022). Cognitive sustainability. Cognitive Sustainability, 1(1). DOI: https://doi.org/htfq