# Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs

arXiv: https://arxiv.org/abs/2005.03082 · HTML: https://mlsanity.com/paper/2005.03082/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2020.

## Built on by (most cited first, 9 of 9)

- [MMCoVaR: Multimodal COVID-19 Vaccine Focused Data Repository for Fake News Detection and a Baseline Architecture for Classification](https://mlsanity.com/paper/2109.06416/) (2021)
- [Modern Dimension Reduction](https://mlsanity.com/paper/2103.06885/) (2021)
- [Analyzing Scientific Publications using Domain-Specific Word Embedding and Topic Modelling](https://mlsanity.com/paper/2112.12940/) (2021)
- [On the intrinsic dimensionality of Covid-19 data: a global perspective](https://mlsanity.com/paper/2203.04165/) (2022)
- [COVID-19 and Big Data: Multi-faceted Analysis for Spatio-temporal Understanding of the Pandemic with Social Media Conversations](https://mlsanity.com/paper/2104.10807/) (2021)
- [Measuring daily-life fear perception change: a computational study in the context of COVID-19](https://mlsanity.com/paper/2107.12606/) (2021)
- [A new LDA formulation with covariates](https://mlsanity.com/paper/2202.11527/) (2022)
- [LaTeX: Language Pattern-aware Triggering Event Detection for Adverse Experience during Pandemics](https://mlsanity.com/paper/2310.03941/) (2023)
- [Exploring a Hybrid Deep Learning Framework to Automatically Discover Topic and Sentiment in COVID-19 Tweets](https://mlsanity.com/paper/2312.01178/) (2023)

## Abstract (arXiv)

This paper illustrates five different techniques to assess the distinctiveness of topics, key terms and features, speed of information dissemination, and network behaviors for Covid19 tweets. First, we use pattern matching and second, topic modeling through Latent Dirichlet Allocation (LDA) to generate twenty different topics that discuss case spread, healthcare workers, and personal protective equipment (PPE). One topic specific to U.S. cases would start to uptick immediately after live White House Coronavirus Task Force briefings, implying that many Twitter users are paying attention to government announcements. We contribute machine learning methods not previously reported in the Covid19 Twitter literature. This includes our third method, Uniform Manifold Approximation and Projection (UMAP), that identifies unique clustering-behavior of distinct topics to improve our understanding of important themes in the corpus and help assess the quality of generated topics. Fourth, we calculated retweeting times to understand how fast information about Covid19 propagates on Twitter. Our analysis indicates that the median retweeting time of Covid19 for a sample corpus in March 2020 was 2.87 hours, approximately 50 minutes faster than repostings from Chinese social media about H7N9 in March 2013. Lastly, we sought to understand retweet cascades, by visualizing the connections of users over time from fast to slow retweeting. As the time to retweet increases, the density of connections also increase where in our sample, we found distinct users dominating the attention of Covid19 retweeters. One of the simplest highlights of this analysis is that early-stage descriptive methods like regular expressions can successfully identify high-level themes which were consistently verified as important through every subsequent analysis.