The construction and application of knowledge graphs have seen a rapid increase across many disciplines in re-cent years. Additionally, the problem of uncovering relationships between developments in the COVID-19 pandemic and social me-dia behavior is of great interest to researchers hoping to curb the spread of the disease. In this paper we present a knowledge graph constructed from COVID-19 related tweets in the Los Angeles area, supplemented with federal and state policy announcements and disease spread statistics. By incorporating dates, topics, and events as entities, we construct a knowledge graph that describes the connections between these useful information. We use natural language processing and change point analysis to extract tweet-topic, tweet-date, and event-date relations. Further analysis on the constructed knowledge graph provides insight into how tweets reflect public sentiments towards COVID-19 related topics and how changes in these sentiments correlate with real-world events. © 2021 IEEE.
An Analysis of COVID-19 Knowledge Graph Construction and Applications