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

被引:456
作者
Ahmed, Wasim [1 ]
Vidal-Alaball, Josep [2 ,3 ]
Downing, Joseph [4 ]
Lopez Segui, Francesc [5 ,6 ,7 ]
机构
[1] Newcastle Univ, 5 Barrack Rd, Newcastle Upon Tyne NE1 4SE, Tyne & Wear, England
[2] Inst Catala Salut, Hlth Promot Rural Areas Res Grp, Gerencia Terr Catalunya Cent, Sant Fruitos De Bages, Spain
[3] Fundacio Inst Univ Recerca Atencio Primaria Salut, Unitat Suport Recerca Catalunya Cent, Sant Fruitos De Bages, Spain
[4] London Sch Econ, European Inst, London, England
[5] Generalitat Catalunya, TIC Salut Social, Barcelona, Spain
[6] Univ Pompeu Fabra, CRES, Barcelona, Spain
[7] Univ Pompeu Fabra, CEXS, Barcelona, Spain
关键词
COVID-19; coronavirus; twitter; misinformation; fake news; 5G; social network analysis; social media; public health; pandemic; MEDIA;
D O I
10.2196/19458
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background: Since the beginning of December 2019, the coronavirus disease (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 keys to combating it. Objective: The aim of this study is 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, March 27, 2020, to Saturday, April 4, 2020) in which the #5GCoronavirus hashtag was trending on Twitter in the United Kingdom. Influential users were analyzed through social network graph clusters. The size of the nodes were ranked by their betweenness centrality score, and the graph's vertices were grouped by cluster using the Clauset-Newman-Moore algorithm. The topics and web sources used were also examined. Results: Social network analysis identified that the two largest network structures consisted of an isolates group and a broadcast group. The analysis also revealed that there was a lack of an authority figure who was actively combating such misinformation. Content analysis revealed that, of 233 sample tweets, 34.8% (n=81) contained views that 5G and COVID-19 were linked, 32.2% (n=75) denounced the conspiracy theory, and 33.0% (n=77) were general tweets not expressing any personal views or opinions. Thus, 65.2% (n=152) of tweets derived from nonconspiracy 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 and targeted interventions oriented to delegitimize the sources of fake information is 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 that are based on fake news. Many social media platforms provide users with the ability to report inappropriate content, which should be used. This study is the first to analyze 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.
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页数:9
相关论文
共 30 条
[1]   Next Generation 5G Wireless Networks: A Comprehensive Survey [J].
Agiwal, Mamta ;
Roy, Abhishek ;
Saxena, Navrati .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2016, 18 (03) :1617-1655
[2]   Public health implications of #ShoutYourAbortion [J].
Ahmed, W. .
PUBLIC HEALTH, 2018, 163 :35-41
[3]  
Ahmed W., 2017, Advances in Research Ethics and Integrity, P79, DOI [DOI 10.1108/S2398-601820180000002004, 10.1108/S2398-601820180000002004]
[4]   Contextualising the 2019 E-Cigarette Health Scare: Insights from Twitter [J].
Ahmed, Wasim ;
Marin-Gomez, Xavier ;
Vidal-Alaball, Josep .
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2020, 17 (07)
[5]   Social media analytics: analysis and visualisation of news diffusion using NodeXL [J].
Ahmed, Wasim ;
Lugovic, Sergej .
ONLINE INFORMATION REVIEW, 2019, 43 (01) :149-160
[6]   Social Media and Fake News in the 2016 Election [J].
Allcott, Hunt ;
Gentzkow, Matthew .
JOURNAL OF ECONOMIC PERSPECTIVES, 2017, 31 (02) :211-235
[7]   The Importance of Debiasing Social Media Data to Better Understand E-Cigarette-Related Attitudes and Behaviors [J].
Allem, Jon-Patrick ;
Ferrara, Emilio .
JOURNAL OF MEDICAL INTERNET RESEARCH, 2016, 18 (08)
[8]  
Brewis H., 2020, Evening Standard
[9]   Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak [J].
Chew, Cynthia ;
Eysenbach, Gunther .
PLOS ONE, 2010, 5 (11)
[10]   #MacronLeaks as a "warning shot" for European democracies: challenges to election blackouts presented by social media and election meddling during the 2017 French presidential election [J].
Downing, Joseph ;
Ahmed, Wasim .
FRENCH POLITICS, 2019, 17 (03) :257-278