# Exploring the evolution of research topics during the COVID-19 pandemic

arXiv: https://arxiv.org/abs/2310.03928 · HTML: https://mlsanity.com/paper/2310.03928/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 64 references that are in the map.
- First posted on arXiv 5 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://mlsanity.com/paper/2104.08663/) (2021)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Fact or Fiction: Verifying Scientific Claims](https://mlsanity.com/paper/2004.14974/) (2020)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [Top2Vec: Distributed Representations of Topics](https://mlsanity.com/paper/2008.09470/) (2020)
- [Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations](https://mlsanity.com/paper/2202.04582/) (2022)
- [COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation](https://mlsanity.com/paper/2007.00576/) (2020)
- [Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec](https://mlsanity.com/paper/1605.02019/) (2016)
- [COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature](https://mlsanity.com/paper/2007.12731/) (2020)
- [Analysis of the influence of political polarization in the vaccination stance: the Brazilian COVID-19 scenario](https://mlsanity.com/paper/2110.03382/) (2021)
- [COVIDScholar: An automated COVID-19 research aggregation and analysis platform](https://mlsanity.com/paper/2012.03891/) (2020)

## Built on by (most cited first, 1 of 1)

- [Capturing research literature attitude towards Sustainable Development Goals: an LLM-based topic modeling approach](https://mlsanity.com/paper/2411.02943/) (2024)

## Abstract (arXiv)

The COVID-19 pandemic has changed the research agendas of most scientific communities, resulting in an overwhelming production of research articles in a variety of domains, including medicine, virology, epidemiology, economy, psychology, and so on. Several open-access corpora and literature hubs were established; among them, the COVID-19 Open Research Dataset (CORD-19) has systematically gathered scientific contributions for 2.5 years, by collecting and indexing over one million articles. Here, we present the CORD-19 Topic Visualizer (CORToViz), a method and associated visualization tool for inspecting the CORD-19 textual corpus of scientific abstracts. Our method is based upon a careful selection of up-to-date technologies (including large language models), resulting in an architecture for clustering articles along orthogonal dimensions and extraction techniques for temporal topic mining. Topic inspection is supported by an interactive dashboard, providing fast, one-click visualization of topic contents as word clouds and topic trends as time series, equipped with easy-to-drive statistical testing for analyzing the significance of topic emergence along arbitrarily selected time windows. The processes of data preparation and results visualization are completely general and virtually applicable to any corpus of textual documents - thus suited for effective adaptation to other contexts.