# An Analysis of COVID-19 Knowledge Graph Construction and Applications

arXiv: https://arxiv.org/abs/2110.04932 · HTML: https://mlsanity.com/paper/2110.04932/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 10 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [TensorLy: Tensor Learning in Python](https://mlsanity.com/paper/1610.09555/) (2016)
- [COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation](https://mlsanity.com/paper/2007.00576/) (2020)
- [Semantic Sensitive TF-IDF to Determine Word Relevance in Documents](https://mlsanity.com/paper/2001.09896/) (2020)
- [Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes](https://mlsanity.com/paper/2105.08683/) (2021)
- [TweetsCOV19 -- A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic](https://mlsanity.com/paper/2006.14492/) (2020)
- [Dynamic topic modeling of the COVID-19 Twitter narrative among U.S. governors and cabinet executives](https://mlsanity.com/paper/2004.11692/) (2020)
- [A Generalized and Adaptive Method for Community Detection](https://mlsanity.com/paper/1406.2518/) (2014)
- [Covid-19 Discourse on Twitter: How the Topics, Sentiments, Subjectivity, and Figurative Frames Changed Over Time](https://mlsanity.com/paper/2103.08952/) (2021)
- [CoVaxxy: A Collection of English-language Twitter Posts About COVID-19 Vaccines](https://mlsanity.com/paper/2101.07694/) (2021)
- [A thematic analysis of highly retweeted early COVID -19 tweets: Consensus, information, dissent, and lockdown life](https://mlsanity.com/paper/2004.02793/) (2020)

## Built on by (most cited first, 2 of 2)

- [Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization](https://mlsanity.com/paper/2403.16222/) (2024)
- [Narrative Analysis of True Crime Podcasts With Knowledge Graph-Augmented Large Language Models](https://mlsanity.com/paper/2411.02435/) (2024)

## Abstract (arXiv)

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.