An Analysis of COVID-19 Knowledge Graph Construction and Applications
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.
Keywords
Cite
@article{arxiv.2110.04932,
title = {An Analysis of COVID-19 Knowledge Graph Construction and Applications},
author = {Dominic Flocco and Bryce Palmer-Toy and Ruixiao Wang and Hongyu Zhu and Rishi Sonthalia and Junyuan Lin and Andrea L. Bertozzi and P. Jeffrey Brantingham},
journal= {arXiv preprint arXiv:2110.04932},
year = {2021}
}