Unlink the Link Between COVID-19 and 5G Networks: An NLP and SNA Based Approach

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.

Health as Battlefield: News and Misinformation in the Early Stage of COVID-19 Outbreak

“…To generate comprehensible explanations, common methods used for the analysis of text-based data are sentiment analysis and LDA (Latent Dirichlet Allocation) topic modeling, which previous studies on COVID-19 have also applied [ 21 , 22 , 23 , 24 ]. For example, some scholars work on sentiment analysis for news during epidemics [ 22 ].…”

Using data mining to track the information spreading on social media about the COVID-19 outbreak

“…With the development of COVID-19 all over the world, information spreading on social media has attracted scholars’ attention. Current studies using social media analysis on the COVID-19 pandemic have focused on sentiment analysis of tweets (Garcia, 2020; Imran et al , 2020; Wrycza and Maślankowski, 2020) and further negative sentiment analysis (Wang et al , 2020a, 2020b), topic identification, link prediction (Bahja and Safdar, 2020), event detection (Rosa et al , 2020), fake news detection (Al-Rakhami and Al-Amri, 2020) and influential user identification (Montes-Orozco et al , 2020). Alharbi et al (2021) introduced a novel technique based on deep learning (DL) that can be used as a surveillance system to identify infected individuals by analysing tweets related to COVID-19.…”

“…Using SNA as an analysis method can support emergency agencies in their understanding of the network structure of user interactions as situations rapidly emerge (Leon et al , 2020; Zhu and Wang, 2020). Using SNA to analyse the network on the COVID-19 pandemic includes COVID-19 transmission analysis based on the citizen interaction community network (Jo et al , 2021; Obadimu et al , 2021; Yie et al , 2021) and information spreading on social media (Ahmed et al , 2020; Al-Shargabi and Selmi, 2021; Bahja and Safdar, 2020; Gruzd and Mai, 2020; Massaro et al , 2021). Studies applied SNA on the topic of COVID-19 mainly focused on the centrality degree (betweenness centrality, closeness centrality, eigenvector centrality), PageRank, cohesive subgroups and core periphery of the networks to detect key nodes in the networks.…”

Guest editorial

“…Time-series analyses have been used to examine the rate of incidences of the COVID-19 cases and deaths (Khayyat et al , 2021). Social network analysis (SNA) has been used to track cases and simulations for modeling the COVID-19 outbreaks (Bahja and Safdar, 2020). Researchers have built models to interpret patterns of public sentiment on disseminating health-related information and assess the political and economic influence of the pandemic.…”