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Using long-term datasets, the study examines relationships between disaster frequency and severity and changes in distress levels across U.S. history. Exploratory Data Analysis and time-series methods reveal temporal patterns, while real-time Reddit data on Hurricane Helene are scraped, labeled for sentiment, and used to train machine learning models to interpret emotional responses. Results support applications for crisis response planning and future analysis.","Claremont Colleges  \nScholarship @ Claremont  \n\n| CMC Senior Theses | CMC Student Scholarship |\n| --- | --- |\n| 2024\u003Cbr>Exploring U.S. Natural Disasters and Psychological Distress: From Time Series Trends to Machine Learning Insights on Hurricane Helene\u003Cbr>Sarah Jane Fullerton\u003Cbr>Follow this and additional works at: [https://scholarship.claremont.edu/cmc_theses](https://scholarship.claremont.edu/cmc_theses)\u003Cbr> Part of the Data Science Commons |  |\n\nRecommended Citation  \nFullerton, Sarah Jane, \"Exploring U.S. Natural Disasters and Psychological Distress: From Time Series Trends to Machine Learning Insights on Hurricane Helene\" (2024) . CMC Senior Theses. 3756.  \n[https://scholarship.claremont.edu/cmc_theses/3756](https://scholarship.claremont.edu/cmc_theses/3756)  \nThis Open Access Senior Thesis is brought to you by Scholarship@Claremont. It has been accepted for inclusion in this collection by an authorized administrator. For more information, please contact [scholarship@claremont.edu](scholarship@claremont.edu).  \nClaremont McKenna College  \nExploring U.S. Natural Disasters and Psychological Distress: From Time Series Trends to Machine Learning Insights on Hurricane Helene  \nsubmitted to  \nProfessor Mark Huber  \nby  \nSarah Jane Fullerton  \nfor  \nSenior Thesis  \nFall 2024  \nDecember 1st, 2024  \n1 Abstract  \nThis research investigates the historical trends of psychological distress in the U.S. in relation to natural disaster occurrences. By analyzing long-term data, we examine how significant natural disasters relate to levels of psychological distress over time. The research employs Exploratory Data Analysis (EDA) and Time Series Analysis to identify patterns and trends between the frequency and intensity of natural disasters and the rise of psychological distress across various periods in U.S. history. Additionally, real-time data from Reddit was collected through a custom-built Reddit web scraper specialized for Hurricane Helene. This dataset was labeled for sentiment and used to train machine learning models for sentiment analysis, providing valuable tools for understanding emotional responses in real-time. Their adaptability makes them applicable for future use in crisis response. The findings of this research offer a dual perspective: understanding the broader historical relationship between natural disasters and psychological distress, and providing insights into emotional reactions to 2024 events.  \nContents  \n1 Abstract 1  \n2 Acronyms 4  \n3 List of Figures 5  \n4 Background 6  \n4.1 Relevant Climate-Mental Health Research .................. 6  \n4.1.1 Existing Research and Gaps ...................... 6  \n5 Research Objectives 8  \n5.1 Focus: The Impact of Natural Disasters on Mental Health ......... 8  \n5.2 Mental Health, Emotional Responses, and Psychological Disorders ... 8  \n5.3 Research Questions and Hypotheses ..................... 8  \n5.3.1 Research Question 1 .......................... 8  \n5.3.2 Research Question 2 .......................... 8  \n6 Methodology 9  \n6.1 Research Design ................................. 9  \n6.2 Data Collection from Public Sources ..................... 9  \n6.3 Data Preprocessing ............................... 10  \n6.3.1 Natural Disaster Dataset ........................ 10  \n6.3.2 Institute For Health Metrics and Evaluation (IHME) Dataset ... 10  \n6.3.3 National Survey on Drug Use and Health (NSDUH) Dataset ... 10  \n6.3.4 Reddit Dataset .............................. 11  \n6.4 Data Validation ................................. 11  \n6.5 Data Analysis Techniques ........................... 12  \n7 Exploratory Data Analysis 13  \n7.1 Natural Disasters ................................ 13  \n7.1.1 Frequency and Temporal Trends ................... 13  \n7.1.2 Geographic Analysis .......................... 17  \n7.2 NSDUH Population ............................... 19  \n7.3 Years Lived with Disability (YLD)s (Global Burden of Disease (GBD) Study) ....................................... 20  \n7.4 Relat","cbCaicmsK6al4UKZ","https://ap.wps.com/l/cbCaicmsK6al4UKZ","pdf",3727420,4,1,48,"English","en",105,"# Abstract\n# Acronyms\n# List of Figures\n# Background\n## Relevant Climate-Mental Health Research\n### Existing Research and Gaps\n# Research Objectives\n## Focus: The Impact of Natural Disasters on Mental Health\n## Mental Health, Emotional Responses, and Psychological Disorders\n## Research Questions and Hypotheses\n### Research Question 1\n### Research Question 2\n# Methodology\n## Research Design\n## Data Collection from Public Sources\n## Data Preprocessing\n### Natural Disaster Dataset\n### Institute For Health Metrics and Evaluation (IHME) Dataset\n### National Survey on Drug Use and Health (NSDUH) Dataset\n### Reddit Dataset\n## Data Validation\n## Data Analysis Techniques\n# Exploratory Data Analysis\n## Natural Disasters\n### Frequency and Temporal Trends\n### Geographic Analysis\n## NSDUH Population\n## Years Lived with Disability (YLD)s (Global Burden of Disease (GBD) Study)\n## Relationships\n# Time-Series with Statistical Analysis\n## U.S. Natural Disaster Temporal Trends\n## Serious Psychological Distress (SPD) Temporal Trends (2004-2022)\n## Depression and Anxiety Induced YLDs Temporal Trends\n## Statistically Significant Findings\n### U.S. Natural Disaster Temporal Trends\n### SPD Temporal Trends (2004–2022)\n### Depression and Anxiety-Induced YLD Trends (1990–2021)\n### Correlations Between Variables\n# Sentiment Analysis\n## Tropical Storm and Hurricane Helene on Reddit\n## Building and Deploying a Reddit Web Scraper\n### API Setup\n### Subreddit Selection\n### Defining Keywords\n### Keyword Matching and Sentiment Classification\n### Data Storage\n### Combining and Cleaning the Data\n### Text Cleaning\n### Final Data Preparation\n### Applications and Benefits\n## Model Testing and Results\n### Linear Regression\n### Deep Learning (LSTM/GRU)\n### Transformer Models (BERT-based)\n## Model Selection and Final Approach\n## Visualizing Sentiments\n## Results\n### Support for Alternative Hypothesis (H1)\n### Potential for Future Analysis\n# Discussion","[{\"question\":\"How does the thesis connect natural disasters to psychological distress in the U.S.?\",\"answer\":\"It analyzes historical patterns to examine how changes in disaster frequency and intensity relate to levels of psychological distress over time across different periods in U.S. history.\"},{\"question\":\"What datasets and methods are used to study these relationships?\",\"answer\":\"The study uses Exploratory Data Analysis and time-series analysis, drawing on long-term public datasets for natural disasters and mental health indicators, supported by data validation and preprocessing steps.\"},{\"question\":\"How is Hurricane Helene-related Reddit data used in the research?\",\"answer\":\"The thesis collects real-time Reddit data using a custom web scraper, labels the text for sentiment, and trains machine learning models to perform sentiment analysis and interpret emotional responses.\"},{\"question\":\"What are the practical implications of the findings?\",\"answer\":\"The research provides both historical insight into the disaster–distress relationship and actionable tools for understanding emotional reactions to 2024 events, supporting future crisis response analysis.\"}]","Exploring U.S. Natural Disasters and Psychological Distress - From Time Series Trends to Machine Learning Insights on Hurricane Helene | 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