Doctoral Dissertations
Keywords and Phrases
Deep Learning; Disaster Management; Natural Language Processing; Situation Awareness; Social Media; Textual Data
Abstract
"In recent years, social media has become a crucial source of real-time data for disaster management, supporting emergency responses when traditional channels like 911 are overcrowded and overwhelmed. It offers authorities valuable data for developing effective strategies, especially when swift actions are essential to save lives. However, the informal language, ambiguous meanings, and irrelevant content on social media pose challenges to accurate classification and hinder the efficient extraction of disaster-relevant information, leading to inefficiencies in emergency response efforts.
This research focuses on seven key questions: i) How can we detect, classify, and analyze hate and offensive tweet emotions during large-scale events? ii) How can deep learning models improve sentiment analysis by identifying low-level emotions in major events? iii) How can fine-grained data enhance crisis communication classification and decision-making in disaster response? iv) How can we assess information relevance and urgency to prioritize emergency responses? v) How can we identify and classify first responders during emergencies? vi) How can we develop an automated fact-checking system to verify disaster claims and combat misinformation? vii) How can unsupervised learning be used to extract keyphrases and detect critical sub-events from unstructured disaster data?
This research addresses disaster management challenges through social media data (e.g. Tweet) by developing models that leverage deep learning and natural language processing (NLP) techniques. These models improve disaster response accuracy and efficiency in identifying situation awareness information, focusing on context, breaking down high-level abstract information into detailed low-level insights, and developing a robust classification system (e.g., to identify relevant first responders). This research’s outcomes help to reduce response time, enhance decision-making, and effectively allocate resources, with practical implications for enhancing emergency response efficiency and saving lives"-- Abstract, p. iv
Advisor(s)
Madria, Sanjay Kumar
Committee Member(s)
Yang, Huiyuan
Nguyen, Long
Morales, Ricardo
Maity, Suman
Degree Name
Ph. D. in Computer Science
Publisher
Missouri University of Science and Technology
Publication Date
Spring 2025
Pagination
xvii, 333 pages
Note about bibliography
Includes_bibliographical_references_(pages 93-96, 120-121, 182-188, 222-226, 248-251, 274-276, 309-312 and 317-329)
Rights
©2024 Ademola Abdulganiyu Adesokan , All Rights Reserved
Document Type
Dissertation - Open Access
File Type
text
Language
English
Thesis Number
T 12450
Recommended Citation
Adesokan, Ademola Abdulganiyu, "Leveraging Deep Learning Models And Social Media Data For Enhanced Situation Awareness In Disaster Management" (2025). Doctoral Dissertations. 3371.
https://scholarsmine.mst.edu/doctoral_dissertations/3371
