Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/33418
Appears in Collections:Management, Work and Organisation Journal Articles
Peer Review Status: Refereed
Title: COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data
Author(s): Ahmed, Wasim
Vidal-Alaball, Josep
Downing, Joseph
López Seguí, Francesc
Contact Email: wasim.ahmed@stir.ac.uk
Keywords: COVID-19
coronavirus
twitter
misinformation
fake news
5G
social network analysis
social media
public health
pandemic
Issue Date: May-2020
Date Deposited: 28-Sep-2021
Citation: Ahmed W, Vidal-Alaball J, Downing J & López Seguí F (2020) COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data. Journal of Medical Internet Research, 22 (5), Art. No.: e19458. https://doi.org/10.2196/19458
Abstract: Background Since the beginning of December 2019 COVID-19 has spread rapidly around the world which has led to increased discussions across online platforms. These conversations have also included various conspiracies shared by social media users. Amongst them a popular theory has linked 5G to the spread of COVID-19 leading to misinformation and the burning of 5G towers in the United Kingdom. The understanding of the drivers of fake news and quick policies oriented to isolate and rebate misinformation are key to combating it. Objective To develop an understanding of the drivers of the 5G COVID-19 conspiracy theory and strategies to deal with such misinformation Methods This paper performs a Social Network Analysis and Content Analysis of Twitter data from a 7-day period, Friday 27 March 2020 to Saturday 04 April 2020, in which the #5GCoronavirus hashtag was trending on Twitter in the United Kingdom. Influential users are analyzed through social network graph clusters. The size of the nodes is ranked by their betweenness centrality score and the graph's vertices are grouped by cluster using the Clauset-Newman-Moore algorithm. Topics and Web sources utilized by users are examined. Results Social Network Analysis identified that the two largest network structures consisted of an isolates group and a broadcast group. The analysis also reveals that there was a lack of authority figure who was actively combating such misinformation. Content analysis reveals that only 35% of individual tweets contained views that 5G and COVID-19 were linked whereas 32% denounced the conspiracy theory and 33% were general tweets not expressing any personal views or opinions. Thus, 65% of tweets derived from non-conspiracy theory supporters which suggests that although the topic attracted high volume only a handful of users genuinely believed the conspiracy. This paper also shows that fake news websites were the most popular Web-source shared by users although YouTube videos were also shared. The study also identified an account whose sole aim was to spread the conspiracy theory on Twitter. Conclusions The combination of quick targeted interventions oriented to delegitimize the sources of fake information are key to reducing their impact. Those users voicing their views against the conspiracy theory, link-baiting, or sharing humorous tweets inadvertently raised the profile of the topic, suggesting that policymakers should insist in the efforts of isolating opinions which are based on fake news. Many social media platforms provide users with the ability to report inappropriate content which should be utilized. This study is the first to analyse the 5G conspiracy theory in the context of COVID-19 on Twitter offering practical guidance to health authorities in how, in the context of a pandemic, rumors may be combated in the future.
DOI Link: 10.2196/19458
Rights: ©Wasim Ahmed, Josep Vidal-Alaball, Joseph Downing, Francesc López Seguí. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 06.05.2020. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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