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2021 | OriginalPaper | Chapter

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

Authors : Iain J. Cruickshank, Kathleen M. Carley

Published in: Information Management and Big Data

Publisher: Springer International Publishing

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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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previous chapter The Identification of Framing Language in Business Leaders’ Speech from the Mass Media

next chapter Calibrated Viewability Prediction for Premium Inventory Expansion

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.

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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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Title: Clustering Analysis of Website Usage on Twitter During the COVID-19 Pandemic
Authors: Iain J. Cruickshank
Kathleen M. Carley
Publisher: Springer International Publishing
Book: Information Management and Big Data
Print ISBN: 978-3-030-76227-8

Electronic ISBN: 978-3-030-76228-5

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-76228-5_28

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