Cross-platform spatiotemporal sentiment trends analysis of COVID-19 vaccine discourse

Abstract The COVID-19 pandemic has sparked intense global discussions about vaccine safety, efficacy, and distribution on social media. It underscored the need to analyze how vaccine-related sentiments propagate across social media and interact with news media articles. Despite extensive research on COVID-19 vaccines, most existing studies examine the sentiment of the COVID-19 vaccine by focusing on social media or news articles in isolation. This study bridges the gap by exploring correlations between these sources through a hierarchical spatiotemporal sentiment analysis framework that integrates social media discussions and mainstream news across global, national (US), and regional (Pennsylvania and Philadelphia) scales. Leveraging over 7 million English tweets and 6,500 news articles alongside physical events, official government records, and demographic data collected between January 2020 and June 2022, we introduce a user location inference method to approximate geographic context. Our approach leverages TriLex, a multi-lexicon sentiment method, and BERTopic to extract nuanced topics, further refined by ChatGPT for enhanced interpretability. The study period was divided into six key intervals, ranging from the beginning of the COVID-19 pandemic to the emergence of the Delta and Omicron variants. The results indicate distinct sentiment patterns in different regions and periods, partially aligning with the NYT’s vaccine-related articles. Although no causal link has been established, our findings highlight the value of correlating multi-scale social media analysis with news articles to address vaccine hesitancy, refine public health messaging, and guide future research on information diffusion in global crises.