Doctoral Dissertations

Keywords and Phrases

COVID-19; Deep learning; Disaster management; Emotion detection; Social media; Text classification


"Social media such as Twitter offers a tremendous amount of data throughout an event or a disastrous situation. Leveraging social media data during a disaster is beneficial for effective and efficient disaster management. Information extraction, trend identification, and determining public reactions might help in the future disaster or even avert such an event. However, during a disaster situation, a robust system is required that can be deployed faster and process relevant information with satisfactory performance in real-time. This work outlines the research contributions toward developing such an effective system for disaster management, where it is paramount to develop automated machine-enabled methods that can provide appropriate tags or labels for further analysis for timely situation-awareness. In that direction, this work proposes machine learning models to identify the people who are seeking assistance using social media during a disaster and further demonstrates a prototype application that can collect and process Twitter data in real-time, identify the stranded people, and create rescue scheduling. In addition, to understand the people’s reactions to different trending topics, this work proposes a unique auxiliary feature-based deep learning model with adversarial sample generation for emotion detection using tweets related to COVID-19. This work also presents a custom Q&A-based RoBERTa model for extracting related phrases for emotions. Finally, with the aim of polarization detection, this research work proposes a deep learning pipeline for political ideology detection leveraging the tweet texts and the expressed emotions in the text. This work also studies and conducts the historical emotion and polarization analysis of the COVID-19 pandemic in the USA and several individual states using tweeter data"--Abstract, page iv.


Madria, Sanjay Kumar

Committee Member(s)

Luo, Tony Tie
Tripathy, Ardhendu
Morales, Ricardo
Dagli, Cihan H., 1949-


Computer Science

Degree Name

Ph. D. in Computer Science


This research has been partially supported by a grant from NSF CNS-1461914.


Missouri University of Science and Technology

Publication Date

Summer 2022

Journal article titles appearing in thesis/dissertation

  • A deep learning approach for tweet classification and rescue scheduling for effective disaster management
  • STIMULATE: A system for real-time information acquisition and learning for disaster management
  • EMOCOV: Machine learning for emotion detection, analysis and visualization using COVID-19 tweets
  • A deep learning approach for ideology detection and polarization analysis during the COVID-19 pandemic leveraging social media


xiv, 146 pages

Note about bibliography

Includes bibliographic references.


© 2022 Md Yasin Kabir, All rights reserved.

Document Type

Dissertation - Open Access

File Type




Thesis Number

T 12161

Electronic OCLC #