Mapping the Pandemic’s Echo: Dynamic Narrative Detection and Spatio-Temporal Sentiment Modeling of COVID-19 Discourse on Twitt
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The COVID-19 pandemic triggered an unprecedented volume of real-time discourse on social media platforms, with Twitter serving as a global forum for public reactions, fears, and evolving narratives. Traditional sentiment analysis approaches treat tweets as independent, static samples, failing to capture the temporal evolution and geographic heterogeneity of public opinion. This paper presents a comprehensive spatio-temporal framework that integrates fine-grained sentiment classification using COVID-Twitter-BERT with dynamic topic modeling via BERTopic to automatically discover and track evolving narratives. Using a corpus of 2.4 million geolocated tweets collected between January 2020 and June 2022, our analysis reveals distinct pandemic phases: early fear-driven narratives about mask shortages (Q1 2020), vaccine optimism followed by polarization (2021), and pandemic fatigue (2022). Regional comparisons show significant differences, with US discourse dominated by freedom-versus-mandate debates while European discussions emphasized collective solidarity. Our framework achieved 76% F1-score in sentiment classification and successfully identified 50 distinct narratives with high coherence scores. This work provides a powerful methodology for real-time epidemiological narrative surveillance and crisis communication monitoring.
Author summary
This study was conducted to overcome the limitations of static, one-dimensional analyses of pandemic-related social media discourse. The researchers developed a novel spatio-temporal framework that integrates COVID-Twitter-BERT for fine-grained sentiment analysis and BERTopic for dynamic topic modeling. Applying this framework to 2.4 million geolocated tweets collected over 30 months, they successfully mapped the evolving “lifecycle” of public narratives—from early fears about mask shortages to later polarization around vaccines and variants. The analysis revealed significant geographic and cultural differences in discourse, with U.S. conversations dominated by freedom-versus-mandate debates while European discussions emphasized collective solidarity. These findings demonstrate that the framework provides a powerful tool for real-time public health surveillance, enabling authorities to detect emerging narratives, monitor sentiment shifts, and design timely, culturally adapted communication strategies for future crises.
