An Analysis of COVID-19 Knowledge Graph Construction and Applications

TL;DR

This paper presents a COVID-19 knowledge graph built from tweets, policies, and disease data to analyze public sentiment and its relation to real-world events, aiding pandemic understanding.

Contribution

It introduces a novel COVID-19 knowledge graph integrating social media, policies, and disease data for comprehensive analysis.

Findings

Insights into public sentiment trends over time

Correlation between social media topics and policy changes

Enhanced understanding of COVID-19 information dissemination

Abstract

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.