2023
DOI: 10.2196/43497
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Twitter's Role in Combating the Magnetic Vaccine Conspiracy Theory: Social Network Analysis of Tweets

Abstract: 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 stru… Show more

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Cited by 4 publications
(2 citation statements)
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References 61 publications
(48 reference statements)
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“…Application of the method Causal inference methods Causal inference in observational studies refers to an intellectual discipline which allows researchers to draw causal conclusions based on data by considering the assumptions, study design, and estimation strategies [20] Causal inference methods, through their well-defined frameworks and assumptions have helped to overcome concerns about bias in the analysis of observational studies [10] Ekline et al 2011 [35] Skerritt et al 2021 [36] DAG (Directed acyclic graph) When considering the effect of one variable on another, DAGs serve as a visual representation of causal assumptions. This structured approach moves the conversation forward by serving as a visual aid that makes underlying relations explicit [37] DAGs can help identify possible confounding for the causal question being considered [37] Pakzad et al 2023 [38] Byrne et al 2019 [39] E-value The E-value is "the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates" [24] The E-value is an intuitive metric to help determine how robust the results of a study are to unmeasured confounding [24] Bender Ignacio et al 2018 [40] Eastwood et al 2018 [41] Use of "big data" Large observational studies have become more popular in the era of big data because of their ability to leverage and analyze multiple sources of observational data [22] such as from population databases, social media, and digital health tools [23] Use of big data in research can help with hypothesis generating, and focuses on the temporal stability of the association [23] Khera et al 2018 [42] Ahmed et al 2023 [43] experimental research methodologies (see Appendix A). One concern is how to apply innovations to new contexts, different topics, and novel areas of research.…”
Section: Strengthsmentioning
confidence: 99%
“…Application of the method Causal inference methods Causal inference in observational studies refers to an intellectual discipline which allows researchers to draw causal conclusions based on data by considering the assumptions, study design, and estimation strategies [20] Causal inference methods, through their well-defined frameworks and assumptions have helped to overcome concerns about bias in the analysis of observational studies [10] Ekline et al 2011 [35] Skerritt et al 2021 [36] DAG (Directed acyclic graph) When considering the effect of one variable on another, DAGs serve as a visual representation of causal assumptions. This structured approach moves the conversation forward by serving as a visual aid that makes underlying relations explicit [37] DAGs can help identify possible confounding for the causal question being considered [37] Pakzad et al 2023 [38] Byrne et al 2019 [39] E-value The E-value is "the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates" [24] The E-value is an intuitive metric to help determine how robust the results of a study are to unmeasured confounding [24] Bender Ignacio et al 2018 [40] Eastwood et al 2018 [41] Use of "big data" Large observational studies have become more popular in the era of big data because of their ability to leverage and analyze multiple sources of observational data [22] such as from population databases, social media, and digital health tools [23] Use of big data in research can help with hypothesis generating, and focuses on the temporal stability of the association [23] Khera et al 2018 [42] Ahmed et al 2023 [43] experimental research methodologies (see Appendix A). One concern is how to apply innovations to new contexts, different topics, and novel areas of research.…”
Section: Strengthsmentioning
confidence: 99%
“…92 [143] A retrospective analysis of the covid-19 infodemic in Saudi Arabia 93 [144] Machine learning in detecting covid-19 misinformation on twitter 94 [145] The Towards a critical understanding of social networks for the feminist movement: Twitter and the women's strike 104 [155] YouTube as a source of information on gout: a quality analysis 105 [156] Social Media, Cognitive Reflection, and Conspiracy Beliefs 106 [157] Using machine learning to compare provaccine and antivaccine discourse among the public on social media: Algorithm development study 107 [158] A social bot in support of crisis communication: 10-years of @LastQuake experience on Twitter 108 [159] Determinants of individuals' belief in fake news: A scoping review determinants of belief in fake news 109 [160] Lack of trust, conspiracy beliefs, and social media use predict COVID-19 vaccine hesitancy 110 [161] Health information seeking behaviors on social media during the covid-19 pandemic among american social networking site users: Survey study 111 [162] Semi-automatic generation of multilingual datasets for stance detection in Twitter 112 [163] Social media content of idiopathic pulmonary fibrosis groups and pages on facebook: Cross-sectional analysis 113 [164] Collecting a large scale dataset for classifying fake news tweets usingweak supervision 114 [165] Youtube videos and informed decision-making about covid-19 vaccination: Successive sampling study 115 [166] The commonly utilized natural products during the COVID-19 pandemic in Saudi Arabia: A cross-sectional online survey 116 [167] A behavioural analysis of credulous Twitter users 117 [73] How do Canadian public health agencies respond to the COVID-19 emergency using social media: A protocol for a case study using content and sentiment analysis 118 [168] The negative role of social media during the COVID-19 outbreak 119 [169] Twitter's Role in Combating the Magnetic Vaccine Conspiracy Theory: Social Network Analysis of Tweets 120 [58] COVID-19, a tale of two pandemics: Novel coronavirus and fake news messaging 121 [170] Concerns discussed on chinese and french social media during the COVID-19 lockdown:comparative infodemiology study based on topic modeling 122 [171] Social media and medical education in the context of the COVID-19 pandemic: Scoping review 123 [172] Rumor Detection Based on SAGNN: Simplified Aggregation Graph Ne...…”
Section: Id Document Referencementioning
confidence: 99%