An innovative way of analyzing COVID topics with LLM
Fahim K Sufi · Journal of Economy and Technology · 2024
In the aftermath of the COVID-19 pandemic, international landscapes have been profoundly reshaped, with shifts in political alliances, economic priorities, and socio-cultural norms. Such evolutions, reflected in the vast expanse of digital conversations, particularly on Twitter, necessitate advanced tools for analysis given their impact on policy and strategy. In this context, the presented study underscores the indispensability of Artificial Intelligence (AI) in discerning intricate patterns from voluminous and multifaceted Twitter data on COVID-19. Through an innovative methodology leveraging AI modalities such as language detection, sentiment analysis, topic analysis, Large Language Model (LLM), regression, clustering, this study distills textual features from 152,070 multilingual tweets across 58 languages, spanning 645 days from 15 July 2021–20 April 2023. Our analyses, automatically identify five pivotal COVID-19 discussion topics and expound on four critical factors—tweet language, retweet count, and positive and negative sentiments—that significantly influence these conversations. In essence, the paper's contributions lie in: 1) unveiling an AI-centric autonomous methodology for deep insights into COVID-19 discussions; 2) empirically validating this approach using a diverse, multilingual dataset that resulted in five key discussion areas; and 3) presenting 52 nuanced AI-generated observations that detail factors influencing these discussions. Comparative literature suggests that our approach offers unparalleled depth in AI-driven analytics related to COVID-19 discourse. In summary, this paper underline the pressing need to harness the power of AI-based Tweet analytics as an indispensable tool in formulating strategic decisions pertaining to disaster responses.