Unlocking Blended Emotions and Underlying Drivers: A Deep Dive into COVID-19 Vaccination Insights on Twitter Across Digital and Physical Realms in New York, Using ChatGPT - Li Yin; Mo Han; Xuanyi Nie (Urban Science)
The conclusions drawn from commonly used topic modeling and sentiment analysis of COVID-19 vaccination discussions on social media often hinge on researchers’ interpretation. These methods inadequately capture the nuanced real-world human emotions and struggle with identifying sarcasm and handling mixed sentiments. This study uses OpenAI API and its Large Language Models (LLM) to analyze tweets to further the discussion on improving vaccination literacy and fostering public trust. We employed LLM to uncover underlying topics associated with non-polarized sentiments to understand public concerns and factors eroding public confidence in vaccination. In addition, the city and regional level analysis provides a more detailed breakdown of spatial differences in the physical realm. Our results showed a blend of positive sentiments toward COVID-19 vaccination in New York State, with an underlying sense of concern. Our topic analysis reveals that social media platforms, which facilitate personal experience sharing, can influence both vaccination acceptance and hesitancy in positive and negative ways. Our method was able to better capture the nuanced emotions of real-world individuals. This approach is less subjective and more consistent than traditional models as it employs ChatGPT’s extensive pre-trained databases instead of relying on individual researchers’ judgments.