SPECTER and k-means was used to cluster the papers. SPECTER is a new method to generate embeddings of documents based on its network of citations and a transformer language model. Every paper was embedded and then clustered with k-means, using k = 20. 

University of California, Berkeley

For text-based clustering, two methods were used: topic modeling and k-means using SPECTER embedding, which is a new method to generate document-level embedding of scientific documents. 

COVID-19 research in Wikipedia methodology: text-based clustering

Colavizza, G. (2020). COVID-19 research in Wikipedia. Quantitative Science Studies, 1-32. https://www.mitpressjournals.org/doi/full/10.1162/qss_a_00080 

COVID-19 research in Wikipedia

Topic modeling looks at patterns of keywords that frequently occur together in a set of documents. They trained and compared topic models using "Latent Dirichlet Allocation," "Correlated Topic Models," "Hierarchical Dirichlet Process," and a range of other topics between five and fifty. 

They found similar results in topic contents and Wikipedia coverage of these topics across models and multiple runs. 

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