Bashir, Shadaab Kawnain, authorRay, Indrakshi, advisorShirazi, Hossein, advisorWang, Haonan, committee member2024-01-012024-01-012023https://hdl.handle.net/10217/237360Social media, particularly Twitter, became the center of public discourse during the COVID-19 global crisis, shaping narratives and perceptions. Recognizing the critical need for a detailed examination of this digital interaction, our research dives into the mechanics of pandemic-related Twitter conversations. This study seeks to understand the many dynamics and effects at work in disseminating COVID-19 information by analyzing and comparing the response patterns displayed by tweets from influential individuals and organizational accounts. To meet the research goals, we gathered a large dataset of COVID-19-related Tweets during the pandemic, which was then meticulously manually annotated. In this work, task-specific transformers and LLM models are used to provide tools for analyzing the digital effects of COVID-19 on sentiment analysis. By leveraging domain-specific models RoBERTa[Twitter] fine-tuned on social media data, this research improved performance in critical task of sentiment analysis. Investigation demonstrates individuals express subjective feelings more frequently compared to organizations. Organizations, however, disseminate more pandemic content in general.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.sentiment analysisCOVID-19Text