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Network Analysis on 5G COVID-19 Conspiracy

Both “5G” and “coronavirus” are hot topics of this year. One is the latest technology standard for broadcast cellular networks, while the other causes the serious pandemic that we are suffering at the moment. However, I was very confused when seeing these two terms put together and discussed seriously. How are they related, and what indeed is the “5G COVID-19 conspiracy”? I started reading with all these questions.

Early this year, there was a theory that “the spread of the coronavirus is associated to the 5G network technology”. This causes a huge number of tweets and retweets spreading this misinformation. To discuss the origin of such “5G coronavirus conspiracy”, how it spreads, which parties are involved, and what can we do to fight against it, the study performed several steps to analyze what was going on.

Firstly, it used the keyword “#5Gcoronavirus” and “5Gcoronavius” to target the English tweets that mentioned this topic. Then it used NodeXL to construct a graph where nodes are users and an edge exists when a user “replies-to” or “mentions” another. Vertices are grouped by cluster according to the Clauset-Newman-Moore algorithm, which is an algorithm to find community structure in a very large network. In the end, a manual content analysis was performed to analyze the purpose of those tweets – are they in favour of or against this conspiracy, or are they tweeting maliciously to make people believe in this conspiracy.

Among all these clusters, there are three most interesting ones that the article discussed – group 1, 2, and 4. Group 1 is an isolated group where those tweeter users tweet without mentioning or replying others. Therefore, the nodes that represent them are isolated from others. They might be tweeting their opinion, but do not contribute that much to the spread of the conspiracy.

Group 2 is the Broadcast group. Those users share contents regard the 5Gcoronavirus topic while also mentioning and replying to others. More users are told about this topic, and some of them eventually become part of this cluster when they started to tweet and mention others. The result is reflected by the increasing size of the group 2 cluster.

The last and most important cluster is the group 4 cluster. They are the accounts that actively spread the conspiracy. Among the total number of 408 Twitter accounts, the manual content analysis provides a report of the top ten influential accounts ranked by betweenness centrality score. We can see that most of them are just normal citizens that very actively spread the conspiracy. The tenth account is an exception. Donald Trump, indeed, did not tweet that much himself regards 5Gcoronavirus. Instead, he was mentioned on tons of tweets to comment on this conspiracy.

It is said that the reason why such misinformation can spread so quickly is due to a lack of authority. It is important that such a public figure or influential person can step out and battle against the conspiracy. I agree with this conclusion – it is too difficult to prevent misinformation from arising, instead, we defeat it when it shows up.

Overall this is a very interesting and detailed article that summarizes this event from both descriptive and technical point of view. I am very impressed by how the knowledge we learned in lecture is so closely related to the real-world event. It is also exciting to analyze the cause and the solution from a technical point of view. I strongly recommend others to also take a look at it when having time.

Reference:

  1. Ahmed W, Vidal-Alaball J, Downing J, López Seguí F
    COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data