A Social Network Analysis of Twitter Data Related to Blood Clots and Vaccines

Ahmed, Wasim; Vidal-Alaball, Josep; Vilaseca, Josep

doi:10.3390/ijerph19084584

Cited by 13 publications

(11 citation statements)

References 20 publications

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“…Our study reveals that a network of mainstream media, important health clinicians, and notable public people were the first to educate the public about the nature and dangers of the Twitter-circulating vaccine magnetization conspiracy [ 37 ]. Other research [ 49 ], such as research examining discussions around blood clot risk related to vaccines, has also found that important health clinicians are critical in disseminating factual information to the public by using social media.…”

Section: Discussionmentioning

confidence: 99%

Twitter's Role in Combating the Magnetic Vaccine Conspiracy Theory: Social Network Analysis of Tweets

Ahmed¹,

Das²,

Vidal-Alaball³

et al. 2023

J Med Internet Res

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Background The popularity of the magnetic vaccine conspiracy theory and other conspiracy theories of a similar nature creates challenges to promoting vaccines and disseminating accurate health information. Objective Health conspiracy theories are gaining in popularity. This study's objective was to evaluate the Twitter social media network related to the magnetic vaccine conspiracy theory and apply social capital theory to analyze the unique social structures of influential users. As a strategy for web-based public health surveillance, we conducted a 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 analyzed by using social network analysis. Data were retrieved from June 1 to June 13, 2021, using the keyword vaccine magnetic. Tweets were retrieved via a dedicated Twitter application programming interface. More specifically, the Academic Track application programming interface was used, and the data were analyzed by using NodeXL Pro (Social Media Research Foundation) and Gephi. Results There were a total of 22,762 connections between Twitter users within the data set. This study found that the most influential user within the network consisted of a news account that was reporting on the magnetic vaccine 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 found that influential users were effective broadcasters against the conspiracy, and their reach extended beyond their own networks of Twitter followers. We emphasize the need for trust in influential users with regard to health information, particularly in the context of the widespread social uncertainty resulting from the COVID-19 pandemic, when public sentiment on social media may be unpredictable. This study highlights the potential of influential users to disrupt information flows of conspiracy theories via their unique social capital.

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Section: Discussionmentioning

confidence: 99%

Twitter's Role in Combating the Magnetic Vaccine Conspiracy Theory: Social Network Analysis of Tweets

Ahmed¹,

Das²,

Vidal-Alaball³

et al. 2023

J Med Internet Res

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this study, three data analysis methods were used, including content analysis, social media engagement analysis, and word frequency & semantic network analysis. We performed content analysis on posts and engagement analysis by totaling the likes, shares, comments, and other reactions [ 6 , 39 ]. The content analysis approach studied complex public health phenomena by transforming a large amount of textual data into systematic themes and categories [ 39 ], which is useful for us to extract key themes from the posts and understand what topics were covered.…”

Section: Methodsmentioning

confidence: 99%

“…Among the different platforms, social media is widely used to facilitate communication between governments and individuals [ 3 ]. An increasing number of studies have investigated the effect of social media on the prevention of COVID-19 and raising awareness of the public [ 4 , 5 ], vaccinations [ 6 , 7 ], and dispelling COVID-19 controversies [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Social Media Engagement in Two Governmental Schemes during the COVID-19 Pandemic in Macao

Jiang

et al. 2022

IJERPH

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Social media engagement is a vehicle for effective communication and engagement between governments and individuals, especially in crises such as the COVID-19 pandemic. Additionally, it can be used to communicate resilience measures and receive feedback. This research aims to investigate public social media engagement with resilience measures related to COVID-19 in Macao. We examined 1107 posts and 791 comments about the government’s face mask supply and consumption voucher schemes on Facebook. Using the Crisis Lifecycle model, we partitioned the data and analyzed the content and engagement of related posts, as well as the word semantics in user comments. Our findings show that social media engagement in these resilience measures is high and positive in the early stages of the pandemic, suggesting social media’s potential in mobilizing society, preserving social resilience, and serving as a two-way communication tool in public health emergencies.

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“…The structure of networks is an important determinant that decides how the network organizes [16]. The topology of the network is formed by the different actors and explains the mechanisms of ideas, information, people, and knowledge traveling from one node to another in the system [6,17]. However, the effect of network structure on tourism industrial performance has rarely been considered in some quantitative discussions.…”

Section: Introductionmentioning

confidence: 99%

Network Structure Influence on Tourism Industrial Performance: A Network Perspective to Explain the Global Tourism Development

Zhu

Liu

2022

Applied Sciences

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Global tourism development can be seen as a tourism network evolution; however, how the network structure influences the tourism industrial performance has not been clearly outlined. This paper utilizes complex network theory to understand the global tourism network changes and detect the global network structure effects on international tourism industrial performance, aiming to explain the tourism development from a network perspective and help to organize international tourism effectively. Using the data of 222 regions’ statistics from 1995 to 2019, this paper explores the influence of the global-level network structure on the tourism industry through Pearson’s correlations test and the individual-level effects through a combination of the gravity model with the mixed-effect model. At the global level, results indicate that a network structure with a higher density or clustering coefficient can improve the global tourism arrivals, but the high value of the network average path length and small-worldness characteristic have negative effects. At the individual level, the node’s characteristics including the high degree, closeness, and betweenness centrality of a region in the network positively improve its international tourism arrivals, while the eigenvector centrality and local clustering coefficient generate negative effects. Additionally, most network structure measurements of a region show stronger effects on its own tourism performance than the regions with which it connects. This paper verifies that the network structure has significant impacts on tourism performance and development, which can aid international tourism development both globally and individually.

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A Social Network Analysis of Twitter Data Related to Blood Clots and Vaccines

Cited by 13 publications

References 20 publications

Twitter's Role in Combating the Magnetic Vaccine Conspiracy Theory: Social Network Analysis of Tweets

Twitter's Role in Combating the Magnetic Vaccine Conspiracy Theory: Social Network Analysis of Tweets

Social Media Engagement in Two Governmental Schemes during the COVID-19 Pandemic in Macao

Network Structure Influence on Tourism Industrial Performance: A Network Perspective to Explain the Global Tourism Development

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