Can you provide a link to the article, or extract the name of their specific method? Unfortunately a quick search for the article only provides access to an abstract. There is certainly the 'cluster' library, but if your work involves natural language process or text mining, you might consider other packages such as 'tm' and 'quanteda'.

They are using DBSCAN to cluster the users by using their tweet-features (e.g., hastags mentions, text etc.) . here you can find a tutorial on text clustering with R (DBSCAN is also used in there):

https://medium.com/@SAPCAI/text-clustering-with-r-an-introduction-for-data-scientists-c406e7454e76

Dear Robert and Kai,

Thank you for your time and answers. Kai: The tutorial that you shared appears quite helpful regarding the clustering aspect of the Vaast's and her colleagues' analyses.

Another critical element is their use of network motif analyses to investigate interdependencies among three emergent roles played by the Twitter users in the connective action episodes. On network motif analysis, the key sources that Vaast and her colleagues cite are:

Benson, A. R., Gleich, D. F., & Leskovec, J. (2016). Higher-order organization of complex networks. Science, 353(6295), 163-166.

Janjić, V., & Pržulj, N. (2012). Biological function through network topology: a survey of the human diseasome. Briefings in functional genomics, 11(6), 522-532.

Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network motifs: simple building blocks of complex networks. Science, 298(5594), 824-827.

Wernicke, S., & Rasche, F. (2006). FANMOD: a tool for fast network motif detection. Bioinformatics, 22(9), 1152-1153.

Any guidance on how these motif analyses can be conducted via R would be greatly appreciated.