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Clustering Analysis of Website Usage on Twitter During the COVID-19 Pandemic

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Information Management and Big Data (SIMBig 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1410))

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Abstract

In this study we analyzed patterns of external website usage on Twitter during the COVID-19 pandemic. We used a multi-view clustering technique, which is able to incorporate multiple views of the data, to cluster the websites’ URLs based on their usage patterns and tweet text that occurs with the URLs. The results of the multi-view clustering of URLs used during the COVID-19 pandemic, from 29 January to 22 June 2020, revealed three, main clusters of URL usage. These three clusters differed significantly in terms of using information from different politically-biased, fake news, and conspiracy theory websites. Our results suggest that there are political biases in how information, to include misinformation, about the COVID-19 pandemic is used on Twitter.

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Notes

  1. 1.

    A Python implementation of this algorithm is available on the lead author’s GitHub page: https://github.com/ijcruic/Multi-view-Clustering-of-Social-Based-Data.

  2. 2.

    https://developer.Twitter.com/en/docs/tweets/filter-realtime/guides/basic-stream-parameters.

  3. 3.

    These bias and fact checking websites are: https://mediabiasfactcheck.com/, http://www.fakenewscodex.com/, and https://www.snopes.com/. For transparency, these labels along with the associated URLs are available in a public repository: https://figshare.com/articles/conference_contribution/Clustering_Analysis_of_Website_ Usage_on_Twitter_during_the_COVID-19_Pandemic/13079657.

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Acknowledgement

This work is supported in part by the Office of Naval Research under the Multidisciplinary University Research Initiatives (MURI) Program award number N000141712675, Near Real Time Assessment of Emergent Complex Systems of Confederates, the Minerva program under grant number N000141512797, Dynamic Statistical Network Informatics, a National Science Foundation Graduate Research Fellowship (DGE 1745016), and by the center for Computational Analysis of Social and Organizational Systems (CASOS). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the ONR or the U.S. government.

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Authors and Affiliations

  1. Center for Computational Analysis of Social and Organizational Systems, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Iain J. Cruickshank & Kathleen M. Carley

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  1. Iain J. Cruickshank
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  2. Kathleen M. Carley
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Correspondence to Iain J. Cruickshank .

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Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Juan Antonio Lossio-Ventura

  2. Visibilia, São Paulo, Brazil

    Jorge Carlos Valverde-Rebaza

  3. University of Valencia, Valencia, Spain

    Eduardo Díaz

  4. Universidad del Pacífico, Lima, Peru

    Hugo Alatrista-Salas

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Cruickshank, I.J., Carley, K.M. (2021). Clustering Analysis of Website Usage on Twitter During the COVID-19 Pandemic. In: Lossio-Ventura, J.A., Valverde-Rebaza, J.C., Díaz, E., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2020. Communications in Computer and Information Science, vol 1410. Springer, Cham. https://doi.org/10.1007/978-3-030-76228-5_28

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  • DOI: https://doi.org/10.1007/978-3-030-76228-5_28

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