Social media analytics: analysis and visualisation of news diffusion using NodeXL

The Influence of Joe Wicks on Physical Activity During the COVID-19 Pandemic: Thematic, Location, and Social Network Analysis of X Data

BACKGROUND

 Social media (SM) was essential in promoting physical activity during the COVID-19 pandemic, especially among people confined to their homes. Joe Wicks, a fitness coach, became particularly popular on SM during this time, posting daily workouts that millions of people worldwide followed.

OBJECTIVE

 This study aims to investigate the influence of Joe Wicks on SM and the impact of his content on physical activity levels among the public.

METHODS

 We used NodeXL Pro (Social Media Research Foundation) to collect data from X (formerly Twitter) over 54 days (March 23, 2020, to May 15, 2020), corresponding to the strictest lockdowns in the United Kingdom. We collected 290,649 posts, which we analyzed using social network analysis, thematic analysis, time-series analysis, and location analysis.

RESULTS

 We found that there was significant engagement with content generated by Wicks, including reposts, likes, and comments. The most common types of posts were those that contained images, videos, and text of young people (school-aged children) undertaking physical activity by watching content created by Joe Wicks and posts from schools encouraging pupils to engage with the content. Other shared posts included those that encouraged others to join the fitness classes run by Wicks and those that contained general commentary. We also found that Wicks' network of influence was extensive and complex. It contained numerous subcommunities and resembled a broadcast network shape. Other influencers added to engagement with Wicks via their networks. Our results show that influencers can create networks of influence that are exhibited in distinctive ways.

CONCLUSIONS

Our study found that Joe Wicks was a highly influential figure on SM during the COVID-19 pandemic and that his content positively impacted physical activity levels among the public. Our findings suggest that influencers can play an important role in promoting public health and that government officials should consider working with influencers to communicate health messages and promote healthy behaviors. Our study has broader implications beyond the status of fitness influencers. Recognizing the critical role of individuals such as Joe Wicks in terms of health capital should be a critical area of inquiry for governments, public health authorities, and policy makers and mirrors the growing interest in health capital as part of embodied and digital experiences in everyday life.

Navigating the asthma network on Twitter: Insights from social network and sentiment analysis

Background

Asthma is a condition in which the airways become inflamed and constricted, causing breathing difficulties, wheezing, coughing, and chest tightness. Social networks can have a substantial effect on asthma management and results. However, no studies of social networks addressing asthma have been undertaken.

Objective

The aim of this research was to identify the significant social network structures, key influencers, top topics, and sentiments of asthma-related Twitter conversations.

Methods

All the tweets collected for this study included the keyword "asthma" or were mentioned in or in replies to tweets that were performed. For this study, a random sample of Twitter data was collected using NodeXL Pro software between December 1, 2022, and January 29, 2023. The data collected includes the user's display name, Twitter handle, tweet text, and the tweet's publishing date and time. After being imported into the Gephi application, the NodeXL data were then shown using the Fruchterman-Reingold layout method. In our study, SNA (Social Network Analysis) metrics were utilized to identify the most popular subject using hashtags, sentiment-related phrases (positive, negative, or neutral), and top influencer by centrality measures (degree, betweenness).

Results

The study collected 48,122 tweets containing the keyword "asthma" or mentioned in replies. News reporters and journalists emerged as top influencers based on centrality measures in Twitter conversations about asthma, followed by government and healthcare institutions. Education, trigger factors (e.g., cat exposure, diet), and associated conditions were highly discussed topics on asthma-related social media posts (e.g., sarscov2, copd). Our study's sentiment analysis revealed that there were 8427 phrases associated neutral comments (18%), 12,582 words reflecting positive viewpoints (26%), and 27,111 words reflecting negative opinions (56%).

Conclusion

This study investigates the relevance of social media influencers, news reporters, health experts, health organizations, and the government in the dissemination and promotion of asthma-related education and awareness during public health information.

The Pulse of Long COVID on Twitter: A Social Network Analysis

BACKGROUND

Long coronavirus disease (COVID) is a complex and multifaceted health condition with a range of severe symptoms that can last for weeks or even months after the acute phase of the illness has passed. Employing social network analysis (SNA) can rapidly provide significant health information to communities related to long COVID. This study aimed to identify the key themes, most influential users, and overall sentiments in the Twitter discourse on long COVID.

METHODS

Data were collected from a Twitter search with the specific keywords "long COVID" from December 1, 2022, to February 22, 2023, using NodeXL Pro. Visualizations, including network graphs and key influencers, were created using Gephi, and sentiment analysis was conducted with Azure Machine.

RESULTS

In total, 119,185 tweets from 94325 users were related to long COVID. Top influencers include medical professionals, researchers, journalists, and public figures, with news media platforms as primary information sources; the most common hashtag was #longCOVID, indicating that it is a significant issue of concern among the Twitter community. In the sentiment analysis, most tweets were negative.

CONCLUSION

The study highlights the importance of critically evaluating information shared by influential users and seeking out multiple sources of information when making health-related decisions. In addition, it emphasizes the value of examining social media conversations to understand public discourse on long COVID and suggests that future researchers could explore the role of social media in shaping public perceptions and behaviors related to health issues. Strategies for enhancing scientific journal engagement and influence in online discussions are discussed as well.

The assessment of Twitter discourse on the new COVID-19 variant, XBB.1.5, through social network analysis

Background

XBB.1.5 is a new subvariant of the SARS-CoV-2 Omicron variant with increased transmissibility and immune escape potential. Twitter has been used to share information and assess this subvariant.

Objectives

This study aims to investigate the channel graph, key influencers, top sources, most trends, and pattern discussion, as well as sentiment measures related to Covid-19 XBB.1.5 variant, by using social network analysis (SNA).

Methods

This experiment involved the collection of Twitter data through the keywords, "XBB.1.5″, and NodeXL, with the obtained information subsequently cleaned to remove duplication and irrelevant tweets. SNA was also performed by using analytical metrics to identify influential users and understand the patterns of connectivity among those discussing XBB.1.5. on Twitter. Moreover, the results were visualized through Gephi software, with sentiment analysis performed by using Azure Machine Learning to categorize tweets into three categories, namely positive, negative, and neutral.

Results

A total of 43,394 XBB.1.5-based tweets were identified, with five key users observed with the highest betweenness centrality score (BCS), namely "ojimakohei"(red), mikito_777 (blue), "nagunagumomo" (green), "erictopol" (orange), w2skwn3 (yellow). The other hand, the in-degree, out-degree, betweenness, closeness, and eigenvector centrality scores of the top 10 Twitter users to explain various patterns and trends and "ojimakohei" was highly central in the network. Most of the top domains (sources) used in XBB.1.5 discourse originated from Twitter, Japanese websites (co.jp and or.jp), and scientific analysis links (biorxiv.org and cdc.gov). This analysis indicated that most of the tweets (61.35 %) were positively classified, accompanied by neutral (22.44 %) and negative (16.20 %) sentiments.

Conclusion

Japan was actively engaged in evaluating the XBB.1.5 variant, with influential users playing a crucial role. The preference for sharing verified sources and the positive sentiment demonstrated a commitment to health awareness. We recommend fostering collaborations between health organizations, the government, and Twitter influencers to address COVID-19-related misinformation and its variants.

Analysing Twitter's Role in Combating the Magnetic Vaccine Conspiracy Theory Using Social Network Analysis

BACKGROUND

The popularity of the magnetic vaccine conspiracy theory, and others of a similar nature, creates challenges to promoting vaccines and disseminating accurate health information.

OBJECTIVE

Health conspiracy theories are gaining in popularity. The study's objective was to evaluate the Twitter social media network related to the magnetic vaccination conspiracy theory and to apply social capital theory to analyse the unique social structures of influential users. As a strategy for online public health surveillance, we employ social network analysis to identify the important opinion leaders sharing the conspiracy, the key websites, and the narratives.

METHODS

A total of 18,706 tweets were retrieved and analysed using social network analysis. Data were retrieved from June 01 to June 13 (2021) using the keyword 'vaccine magnetic'. Tweets were retrieved via a dedicated Twitter Application Programming Interface (API). More specifically, the Academic Track API was used, and the data were analysed using NodeXL Pro and Gephi.

RESULTS

There were a total of 22,762 connections between Twitter users within the dataset. The study found that the most influential user within the network consisted of a news account that was reporting on the conspiracy. There were also several other users that became influential such as an epidemiologist, a health economist, and a retired sports athlete who exerted their social capital within the network.

CONCLUSIONS

Our study finds that influential users were effective broadcasters against the conspiracy, and their reach extended beyond their own network of Twitter followers. We emphasise the need for trust in contact with influential users concerning health information, particularly in the context of widespread social uncertainty resulting from the pandemic, when public sentiment on social media may be unpredictable. The study highlights the potential of influential users to disrupt information flows of conspiracy theories due to their unique social capital.

CLINICALTRIAL

Analyzing Discussions Around Rural Health on Twitter During the COVID-19 Pandemic: Social Network Analysis of Twitter Data

Background

Individuals from rural areas are increasingly using social media as a means of communication, receiving information, or actively complaining of inequalities and injustices.

Objective

The aim of our study is to analyze conversations about rural health taking place on Twitter during a particular phase of the COVID-19 pandemic.

Methods

This study captured 57 days' worth of Twitter data related to rural health from June to August 2021, using English-language keywords. The study used social network analysis and natural language processing to analyze the data.

Results

It was found that Twitter served as a fruitful platform to raise awareness of problems faced by users living in rural areas. Overall, Twitter was used in rural areas to express complaints, debate, and share information.

Conclusions

Twitter could be leveraged as a powerful social listening tool for individuals and organizations that want to gain insight into popular narratives around rural health.

Relationship between new media literacy (NML) and web-based fake news epidemic control: a systematic literature review

Purpose

This study aims to investigate the current practices being implemented against the dissemination of fake online news, identify the relationship of new media literacy (NML) with fake news epidemic control and find out the challenges in identifying valid sources of information.

Design/methodology/approach

To accomplish constructed objectives of this study, a systematic literature review (SLR) was conducted. The authors carried out the “Preferred Reporting Items for the Systematic Review and Meta-analysis” guidelines as a research methodology. The data were retrieved from ten world’s leading digital databases and online tools. A total of 25 key studies published in impact factor (IF) journals were included for systematic review vis-à-vis standard approaches.

Findings

This study revealed trending practices to control fake news consisted of critical information literacy, civic education, new thinking patterns, fact-checkers, automatic fake news detection tools, employment of ethical norms and deep learning via neural networks. Results of the synthesized studies revealed that media literacy, web literacy, digital literation, social media literacy skills and NML assisted acted as frontline soldiers in combating the fake news war. The findings of this research also exhibited different challenges to control fake news perils.

Research limitations/implications

This study provides pertinent theoretical contributions in the body of existing knowledge through the addition of valuable literature by conducting in-depth systematic review of 25 IF articles on a need-based topic.

Practical implications

This scholarly contribution is fruitful and practically productive for the policymakers belonging to different spectrums to effectively control web-based fake news epidemic.

Social implications

This intellectual piece is a benchmark to address fake news calamities to save the social system and to educate citizens from harms of false online stories on social networking websites.

Originality/value

This study vivifies new vistas via a reinvigorated outlook to address fake news perils embedded in dynamic, rigorous and heuristic strategies for redefining a predetermined set of social values.

The #VaccinesWork Hashtag on Twitter in the Context of the COVID-19 Pandemic: Network Analysis

Background

Vaccination is one of the most successful public health interventions for the prevention of COVID-19. Toward the end of April 2021, UNICEF (United Nations International Children’s Emergency Fund), alongside other organizations, were promoting the hashtag #VaccinesWork.

Objective

The aim of this paper is to analyze the #VaccinesWork hashtag on Twitter in the context of the COVID-19 pandemic, analyzing the main messages shared and the organizations involved.

Methods

The data set used in this study consists of 11,085 tweets containing the #VaccinesWork hashtag from the 29th to the 30th of April 2021. The data set includes tweets that may not have the hashtag but were replies or mentions in those tweets. The data were retrieved using NodeXL, and the network graph was laid out using the Harel-Koren fast multiscale layout algorithm.

Results

The study found that organizations such as the World Health Organization, UNICEF, and Gavi were the key opinion leaders and had a big influence on the spread of information among users. Furthermore, the most shared URLs belonged to academic journals with a high impact factor. Provaccination users had other vaccination-promoting hashtags in common, not only in the COVID-19 scenario.

Conclusions

This study investigated the discussions surrounding the #VaccinesWork hashtag. Social media networks containing conspiracy theories tend to contain dubious accounts leading the discussions and are often linked to unverified information. This kind of analysis can be useful to detect the optimal moment for launching health campaigns on Twitter.

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

Purpose

COVID-19, a causative agent of the potentially fatal disease, has raised great global public health concern. Information spreading on the COVID-19 outbreak can strongly influence people behaviour in social media. This paper aims to question of information spreading on COVID-19 outbreak are addressed with a massive data analysis on Twitter from a multidimensional perspective.

Design/methodology/approach

The evolutionary trend of user interaction and the network structure is analysed by social network analysis. A differential assessment on the topics evolving is provided by the method of text clustering. Visualization is further used to show different characteristics of user interaction networks and public opinion in different periods.

Findings

Information spreading in social media emerges from different characteristics during various periods. User interaction demonstrates multidimensional cross relations. The results interpret how people express their thoughts and detect topics people are most discussing in social media.

Research limitations/implications

This study is mainly limited by the size of the data sets and the unicity of the social media. It is challenging to expand the data sets and choose multiple social media to cross-validate the findings of this study.

Originality/value

This paper aims to find the evolutionary trend of information spreading on the COVID-19 outbreak in social media, including user interaction and topical issues. The findings are of great importance to help government and related regulatory units to manage the dissemination of information on emergencies, in terms of early detection and prevention.

Exploring nurses' online perspectives and social networks during a global pandemic COVID-19

Objectives

Examine the online interactions, social networks, and perspectives of nursing actors on COVID‐19 from conversations on Twitter to understand how the profession responded to this global pandemic.

Design

Mixed methods.

Sample

Ten‐thousand five‐hundred and seventy‐four tweets by 2790 individuals and organizations.

Measurements

NodeXL software was used for social network analysis to produce a network visualization. The betweenness centrality algorithm identified key users who were influential in COVID‐19 related conversations on Twitter. Inductive content analysis enabled exploration of tweet content. A communicative figurations framework guided the study.

Results

Nursing actors formed different social groupings, and communicated with one another across groups. Tweets covered four themes; (1) outbreak and clinical management of the infectious disease, (2) education and information sharing, (3) social, economic, and political context, and (4) working together and supporting each other.

Conclusion

In addition to spreading knowledge, nurses tried to reach out through social media to political and healthcare leaders to advocate for improvements needed to address COVID‐19. However, they primarily conversed within their own professional community. Action is needed to better understand how social media is and can be used by nurses for health communication, and to improve their preparedness to be influential on social media beyond the nursing community.

Breaching Learners' Social Distancing through Social Media during the COVID-19 Pandemic

Higher education has been shifted toward blended learning during the COVID-19 pandemic. An increase in social media usage intensity and reduced face-to-face interaction due to the COVID-19 pandemic urged instructional communication researchers to revisit the dynamics of learners’ group development in terms of their socialization and academic performance during the COVID-19 crisis. This research aimed to determine the mediating role of social media sociability between face-to-face socialization and academic performance of higher education students in blended learning environments during the COVID-19 pandemic. It was also the aim of the study to determine the moderating effect of social media usage intensity on social media sociability. A cross-sectional survey was conducted with the students (n = 340) enrolled in science teacher education departments of universities in Pakistan. Partial least squares structural equation modeling (PLS-SEM) was used for multivariate analysis. Results revealed that face-to-face socialization gave an essential start to develop a learning group. However, when face-to-face socialization was reduced due to the COVID-19 pandemic, it was mediated by social media usage in blended learning environments to increase their socialization and academic performance during the crisis. The findings of the study are useful for higher education institutions to adopt social media strategies for students’ socialization during the crisis.

Message sharing and verification behaviour on social media during the COVID-19 pandemic: a study in the context of India and the USA

Purpose

Technology has eased access to information. During the ongoing COVID-19 pandemic, ease of access and transmission of information via social media has led to ambiguity, misinformation and uncertainty. This research studies the aforementioned behaviours of information sharing and verification related to COVID-19, in the context of social media.

Design/methodology/approach

Two studies have been carried out. Study 1, with Indian social media users, is a two-factor between-subjects experimental design that investigated the effect of message polarity (positive versus negative) and message type (news versus rumour) on the dissemination and verification behaviour of COVID-19-related messages. The study also investigated the mediation of perceived message importance and health anxiety. Study 2 is a replica study conducted with US users.

Findings

The study finding revealed significantly higher message sharing for news than rumour. Further, for the Indian users, message with positive polarity led to higher message sharing and message with negative polarity led to higher verification behaviour. On the contrary, for the US users, message with negative polarity led to higher message sharing and message with positive polarity led to higher verification behaviour. Finally, the study revealed message importance mediates the relationship of message type and message sharing behaviour for Indian and US users; however, health anxiety mediation was significant only for Indian users.

Practical implications

The findings offer important implications related to information regulation during a health crisis. Unverified information sharing is harmful during a pandemic. The study sheds light on this behaviour such that stakeholders get insights and better manage the information being disseminated.

Originality/value

The study investigates the behaviour of sharing and verification of social media messages between users containing health information (news and rumour) related to the ongoing COVID-19 pandemic.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-07-2020-0282

Twitter dialogue: an analysis of Pakistani politicians’ information sharing

Purpose

The use of Twitter by political parties and politicians has been well studied in developed countries. However, there is a lack of empirical work, which has examined the use of Twitter in developing countries. This study aims to explore the information-sharing patterns of Pakistani politicians through Twitter accounts during the pre-election campaign of 2018.

Design/methodology/approach

Data of three weeks of the official party accounts and the politicians running for prime minister were analysed. The mixed-methods approach has been used to analyse quantitative and qualitative data retrieved through Twitonomy.

Findings

It was found that the most active Twitter account belonged to the winning party. The prominent Twitter account functions were a call to vote, promotional Tweets, promises and Tweeting about party developments. The present study provides evidence that there is a difference between the Tweeting behaviour of established and emerging parties. The emerging party heavily posted about changing traditional norms/culture/practices.

Practical implications

The study contributed to existing knowledge and has practical implications for politicians, citizens and social media planners.

Originality/value

The present study was designed carefully and based on empirical research. The study is unique in its nature to fill the research and knowledge gap by adding a variety of Twitter functions used by politicians.

A mixed-method analysis of the #SugarTax debate on Twitter

OBJECTIVE

To explore the #SugarTax debate on Twitter to assess actors involved, their connections and the topics being discussed during the implementation and first anniversary of the UK Soft Drink Industry Levy.

DESIGN

The structure of the #SugarTax debate on Twitter was assessed using social network analysis. The actors involved, their connections and the topics of discussion taking place were also explored using content, sentiment and thematic analyses.

SETTING

Twitter between 2017 and 2019.

PARTICIPANTS

Twitter users engaging in discussions relating to the hashtag 'SugarTax'.

RESULTS

Tweets (n 5366) posted between 5 August 2017 and 7 May 2019 containing #SugarTax were downloaded from Twitter using Network Overview for Discovery and Exploration in Excel. The network included 1883 users, with 686 unique edges and 4679 edges with duplicates. The majority of tweets were negative in sentiment, when assessed by both automatic (64 %, n 141) and manual sentiment analysis (52 %, n 115) methods. Nine key themes were identified and grouped into two groups according to 'support for a sugar or SSB tax' or 'opposition for a sugar or SSB tax'.

CONCLUSIONS

Twitter was used as a platform for debating the benefits and limitations of sugar-sweetened beverage taxes. The findings indicate that numerous actors are involved in the debates on Twitter, with advocates and lobbyists using the platform to raise support for their campaigns and reshape public perceptions. The findings and the methods used may be of interest to policymakers as well as to academics and members of the public looking to explore and engage in policy debates.

Tag analysis as a tool for investigating information behaviour: comparing fan-tagging on Tumblr, Archive of Our Own and Etsy

Purpose

This article describes the third part of a three-stage study investigating the information behaviour of fans and fan communities, the first stage of which is described in the study by Price and Robinson (2017).

Design/methodology/approach

Using tag analysis as a method, a comparative case study was undertaken to explore three aspects of fan information behaviour: information gatekeeping; classifying and tagging and entrepreneurship and economic activity. The case studies took place on three sites used by fans–Tumblr, Archive of Our Own (AO3) and Etsy. Supplementary semi-structured interviews with site users were used to augment the findings with qualitative data.

Findings

These showed that fans used tags in a variety of ways quite apart from classification purposes. These included tags being used on Tumblr as meta-commentary and a means of dialogue between users, as well as expressors of emotion and affect towards posts. On AO3 in particular, fans had developed a practice called “tag wrangling” to mitigate the inherent “messiness” of tagging. Evidence was also found of a “hybrid market economy” on Etsy fan stores. From the study findings, a taxonomy of fan-related tags was developed.

Research limitations/implications

Findings are limited to the tagging practices on only three sites used by fans during Spring 2016, and further research on other similar sites are recommended. Longitudinal studies of these sites would be beneficial in understanding how or whether tagging practices change over time. Testing of the fan-tag taxonomy developed in this paper is also recommended.

Originality/value

This research develops a method for using tag analysis to describe information behaviour. It also develops a fan-tag taxonomy, which may be used in future research on the tagging practices of fans, which heretofore have been a little-studied section of serious leisure information users.

The Instagram Infodemic: Cobranding of Conspiracy Theories, Coronavirus Disease 2019 and Authority-Questioning Beliefs

The novel coronavirus 2019 pandemic has brought about an overabundance of misinformation concerning the virus (SARS-CoV-2) and the coronavirus disease 2019 (COVID-19) it causes spreading rapidly on social media. While some more obviously untrustworthy sources may be easier for social media filters to identify and remove, an early feature was the cobranding of COVID-19 misinformation with other types of misinformation. To examine this, the top 10 Instagram posts (in English) were collected every day for 10 days (April 21-30th, 2020) for each of the hashtags #hoax, #governmentlies, and #plandemic. The #hoax was selected first as it is commonly used in conspiracy theory posts, and #governmentlies because it was the most commonly cotagged with #hoax. For comparison, we selected #plandemic as the most popular cotagged hashtag that was clearly COVID-19-related. This resulted in 300 Instagram posts available for our analysis. We conducted a content analysis by coding the themes contained in the posts, both for the images and the text caption shared by the Instagram users (including hashtags). The broad theme of general mistrust was the most common, including the idea that the government and/or media has fabricated or hidden information pertaining to COVID-19. Conspiracy theories were the second-most frequent theme among posts. Overall, COVID-19 was frequently presented in association with authority-questioning beliefs. Developing an understanding of how the public shares misinformation on COVID-19 alongside conspiracy theories and authority-questioning statements can aid public health officials and policymakers in limiting the spread of potentially life-threatening health misinformation.

A Social Network Analysis of Tweets Related to Masks during the COVID-19 Pandemic

BACKGROUND

High compliance in wearing a mask is a crucial factor for stopping the transmission of COVID-19. Since the beginning of the pandemic, social media has been a key communication channel for citizens. This study focused on analyzing content from Twitter related to masks during the COVID-19 pandemic.

METHODS

Twitter data were collected using the keyword "mask" from 27 June 2020 to 4 July 2020. The total number of tweets gathered were n = 452,430. A systematic random sample of 1% (n = 4525) of tweets was analyzed using social network analysis. NodeXL (Social Media Research Foundation, California, CA, USA) was used to identify users ranked influential by betweenness centrality and was used to identify key hashtags and content.

RESULTS

The overall shape of the network resembled a community network because there was a range of users conversing amongst each other in different clusters. It was found that a range of accounts were influential and/or mentioned within the network. These ranged from ordinary citizens, politicians, and popular culture figures. The most common theme and popular hashtags to emerge from the data encouraged the public to wear masks.

CONCLUSION

Towards the end of June 2020, Twitter was utilized by the public to encourage others to wear masks and discussions around masks included a wide range of users.

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

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.