An Analysis of COVID-19 Knowledge Graph Construction and Applications

Dominic Flocco; Bryce Palmer-Toy; Ruixiao Wang; Hongyu Zhu; Rishi Sonthalia; Junyuan Lin; Andrea L. Bertozzi; P. Jeffrey Brantingham

arXiv · 2110.04932

An Analysis of COVID-19 Knowledge Graph Construction and Applications

Dominic Flocco·Bryce Palmer-Toy·Ruixiao Wang·Hongyu Zhu·Rishi Sonthalia·Junyuan Lin·Andrea L. Bertozzi·P. Jeffrey Brantingham

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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.

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Dominic Flocco, Bryce Palmer-Toy, Ruixiao Wang, Hongyu Zhu, Rishi Sonthalia, Junyuan Lin, Andrea L. Bertozzi, P. Jeffrey Brantingham. 2021-10-10. An Analysis of COVID-19 Knowledge Graph Construction and Applications. https://arxiv.org/abs/2110.04932

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