Public Healthcare Informatics for COVID-19 from Social Media Data

The COVID-19 pandemic has profoundly affected various facets of society. This research explores the discourse on social media, specifically "X" (Twitter), to understand public sentiment and needs regarding healthcare supply chain challenges, such as the availability of testing kits, oxygen supplies, and hospital beds, during the pandemic. By identifying pertinent opinionated keyphrases and employing advanced natural language processing (NLP) techniques, we sifted through over 3.9 million tweets to collect health informatics. The content of these tweets is analyzed to investigate the word trends and sentiments. The extracted sentiments were then organized into ten distinct categories using the K-means clustering method that pointed out top-ten healthcare supply needs. The findings underscore the value of social media as a tool for gathering timely and actionable insights on healthcare supply chain logistics, offering critical data for effective response strategies during health crises.