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Unlink the link between COVID-19 and 5G Networks: an NLP and SNA based approach

Research Output:
Contribution to journal
Article
 Peer-review

Open access

Abstract

Social media facilitates rapid dissemination of information for both factual and fictional information. The spread of non-scientific information through social media platforms such as Twitter has potential to cause damaging consequences. Situations such as the COVID-19 pandemic provides a favourable environment for misinformation to thrive. The upcoming 5G technology is one of the recent victims of misinformation and fake news and has been plagued with misinformation about the effects of its radiation. During the COVID-19 pandemic, conspiracy theories linking the cause of the pandemic to 5G technology have resonated with a section of people leading to outcomes such as destructive attacks on 5G towers. The analysis of the social network data can help to understand the nature of the information being spread and identify the commonly occurring themes in the information. The natural language processing (NLP) and the statistical analysis of the social network data can empower policymakers to understand the misinformation being spread and develop targeted strategies to counter the misinformation. In this paper, NLP based analysis of tweets linking COVID-19 to 5G is presented. NLP models including Latent Dirichlet allocation (LDA), sentiment analysis (SA) and social network analysis (SNA) were applied for the analysis of the tweets and identification of topics. An understanding of the topic frequencies, the inter-relationships between topics and geographical occurrence of the tweets allows identifying agencies and patterns in the spread of misinformation and equips policymakers with knowledge to devise counter-strategies.

Publication Information

Output type

Research Output:
Contribution to journal
Article
 Peer-review

Original language

English

Journal (Volume, Issue Number)

IEEE Access (Volume 8)

Publication milestones

  • Accepted/In press - 14/11/2020
  • Published - 18/11/2020

Publication status

Published - 18/11/2020

Publication IDs

  • handle.net: 10547/625057
  • Scopus: 85184696965

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