  The construction and application of knowledge graphs have seen a rapid
increase across many disciplines in recent years. Additionally, the problem of
uncovering relationships between developments in the COVID-19 pandemic and
social media 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.
