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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Apr 18, 2020
Open Peer Review Period: Apr 18, 2020 - Apr 21, 2020
Date Accepted: Apr 25, 2020
Date Submitted to PubMed: Apr 30, 2020
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data

Ahmed W, Vidal-Alaball J, Downing J, Lopez Seguí F

COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data

J Med Internet Res 2020;22(5):e19458

DOI: 10.2196/19458

PMID: 32352383

PMCID: 7205032

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Dangerous Messages or Satire? Analysing the Conspiracy Theory Linking 5G to COVID-19 through Social Network Analysis

  • Wasim Ahmed; 
  • Josep Vidal-Alaball; 
  • Joseph Downing; 
  • Francesc Lopez Seguí



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.


To develop an understanding of the drivers of the 5G COVID-19 conspiracy theory and strategies to deal with such misinformation


This paper performs a Social Network Analysis and Content Analysis of Twitter data from a 7-day period in which the #5GCoronavirus hashtag was trending on Twitter. 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.


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.


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. 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.


Please cite as:

Ahmed W, Vidal-Alaball J, Downing J, Lopez Seguí F

Dangerous Messages or Satire? Analysing the Conspiracy Theory Linking 5G to COVID-19 through Social Network Analysis

JMIR Preprints. 18/04/2020:19458

DOI: 10.2196/preprints.19458


PMID: 32352383

PMCID: 7205032

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