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Understanding the characteristics of COVID-19 misinformation communities through graphlet analysis

Ashford, James, Turner, Liam ORCID:, Whitaker, Roger ORCID:, Preece, Alun ORCID: and Felmlee, Diane 2022. Understanding the characteristics of COVID-19 misinformation communities through graphlet analysis. Online Social Networks and Media 27 , 100178. 10.1016/j.osnem.2021.100178

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Online social networks serve as a convenient way to connect, share, and promote content with others. As a result, these networks can be used with malicious intent, causing disruption and harm to public debate through the sharing of misinformation. However, automatically identifying such content through its use of natural language is a significant challenge compared to our solution which uses less computational resources, language-agnostic and without the need for complex semantic analysis. Consequently alternative and complementary approaches are highly valuable. In this paper, we assess content that has the potential for misinformation and focus on patterns of user association with online social media communities (subreddits) in the popular Reddit social media platform, and generate networks of behaviour capturing user interaction with different subreddits. We examine these networks using both global and local metrics, in particular noting the presence of induced substructures (graphlets) assessing posts from 96,634 users. From subreddits identified as having potential for misinformation, we note that the associated networks have strongly defined local features relating to node degree — these are evident both from analysis of dominant graphlets and degree-related global metrics. We find that these local features support high accuracy classification of subreddits that are categorised as having the potential for misinformation. Consequently we observe that induced local substructures of high degree are fundamental metrics for subreddit classification, and support automatic detection capabilities for online misinformation independent from any particular language.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Crime and Security Research Institute (CSURI)
Additional Information: This is an open access article under the CC BY license (
Publisher: Elsevier
ISSN: 2468-6964
Funders: Ministry of Defence
Date of First Compliant Deposit: 22 November 2021
Date of Acceptance: 8 October 2021
Last Modified: 02 May 2023 23:05

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