An Analysis of COVID-19 Knowledge Graph Construction and Applications
This work addresses the need for researchers to analyze social media data to curb disease spread, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of understanding relationships between COVID-19 developments and social media behavior by constructing a knowledge graph from tweets, policy announcements, and disease statistics in Los Angeles, resulting in insights into how public sentiments correlate with real-world events.
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.