preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202407.2291.v1 (registering DOI)

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 26 July 2024 / Approved: 29 July 2024 / Online: 29 July 2024 (10:49:35 CEST)

Countless disasters have resulted from climate change, with severe damage to infrastructure and the economy. Their societal impacts require mental health services for the millions affected. To prepare for and respond effectively to these events, it is important to understand people’s emotions and incidents both before and after a disaster strikes. The emotions and other life incidents expressed on Twitter during the 2017 Hurricane Harvey were mined in this case study. Between August 20 and August 30, we collected a dataset of approximately 400,000 public tweets that related to the storm. Using a BERT-based model, we predicted the emotions attached to each tweet. After that, we used various natural language processing techniques to analyze the tweets themselves to understand what kind of ’topics’ were being discussed when people expressed certain ’emotions’. Applying the Latent Dirichlet Allocation (LDA) technique for topic modeling sidesteps the need for manual content analysis and allows us to find meaningful patterns in the data. Results show that there is a significant change of emotions occurred when the hurricane is at most devastating and many of life incidents become pressing demands/concerns from population such as evacuation plan, animal safety, safety update, public policy, helps, flood, and fake news.

emotional health; climate change; life incidents; natural language processing; topic modeling; social media

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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