Topic evolution in social media discourse during global health emergencies

Abstract

Abstract Global health emergencies, such as the COVID-19 pandemic, have demonstrated the power and volatility of social media as a platform for public communication. Millions of people worldwide relied on digital platforms like Twitter and Facebook for real-time information, updates, and expressions of emotion. However, this rapid flow of information also led to the widespread diffusion of misinformation and changing patterns of discourse; a phenomenon that calls for a systematic investigation. This study explores topic evolution in social media discourse during global health emergencies using Natural Language Processing methods, including topic modelling (LDA and BERTopic), sentiment analysis (VADER and BERTweet), and geospatial trend analysis. A dataset of 70,452 COVID-19-related tweets collected from Nigeria between March 2020 and December 2022 was analyzed using Natural Language Processing (NLP) and data mining techniques. Topic modelling (Latent Dirichlet Allocation and BERTopic), sentiment analysis (VADER and BERTweet), and geospatial trend mapping were employed to examine the interplay between sentiment and thematic focus over time. The results reveal five dominant thematic clusters— public health awareness , lockdown and economy , vaccine and misinformation , recovery and hope , and political accountability . Temporal and spatial analyses further indicate that public sentiment and attention evolved in alignment with pandemic milestones such as lockdown enforcement, vaccine rollout, and policy controversies. The study contributes a hybrid analytical framework that integrates sentiment and topic evolution modelling for infodemic monitoring. By mapping discourse evolution across phases of a crisis, the research provides actionable insights for policymakers, communication strategists, and public health agencies to design adaptive and evidence-driven responses during global health emergencies.