The web and public confidence in MMR vaccination in Italy

Aquino, Francesco; Donzelli, Gabriele; Franco, E; Privitera, Gaetano Pierpaolo; Lopalco, Pietro Luigi; Carducci, Annalaura

doi:10.1016/j.vaccine.2017.07.029

Cited by 123 publications

(116 citation statements)

References 7 publications

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“…The classic example is vaccination, where effective protection of the population requires achievement of levels of uptake sufficient to achieve herd immunity. This review confirms that misconceptions about MMR vaccine and autism, in particular, remain prevalent on social media (Aquino et al, 2017;Chen et al, 2018a,b). Other topics share scientific uncertainty, with the authorities unable to provide confident explanations or advice, as with newly emerging virus infections such as Ebola and Zika viruses (Basch et al, 2017;Fung et al, 2016;Sommariva et al, 2018).…”

Section: Resultssupporting

confidence: 80%

“…Krishna's (2017) study of active propagators of these messages found that those who were knowledge-deficient and vaccine-averse exhibit higher levels of activity than those who are not. Aquino et al (2017) reported a significant inverse correlation between MMR vaccination coverage and online searches and social network activity on "autism and MMR vaccine". Taken as a whole, the research identifies anti-vaxxer and members of online communities favouring conspiracy theories as sources or propagators of misinformation, with discussions tending to revolve around rhetorical and personal arguments that induce negative emotions (fear, anger, sadness).…”

Section: Vaccines and Communicable Diseasesmentioning

confidence: 99%

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Systematic Literature Review on the Spread of Health-related Misinformation on Social Media

Wang

McKee

Torbica

et al. 2019

Social Science & Medicine

1,243

806

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A B S T R A C TContemporary commentators describe the current period as "an era of fake news" in which misinformation, generated intentionally or unintentionally, spreads rapidly. Although affecting all areas of life, it poses particular problems in the health arena, where it can delay or prevent effective care, in some cases threatening the lives of individuals. While examples of the rapid spread of misinformation date back to the earliest days of scientific medicine, the internet, by allowing instantaneous communication and powerful amplification has brought about a quantum change. In democracies where ideas compete in the marketplace for attention, accurate scientific information, which may be difficult to comprehend and even dull, is easily crowded out by sensationalized news. In order to uncover the current evidence and better understand the mechanism of misinformation spread, we report a systematic review of the nature and potential drivers of health-related misinformation. We searched PubMed, Cochrane, Web of Science, Scopus and Google databases to identify relevant methodological and empirical articles published between 2012 and 2018. A total of 57 articles were included for full-text analysis. Overall, we observe an increasing trend in published articles on health-related misinformation and the role of social media in its propagation. The most extensively studied topics involving misinformation relate to vaccination, Ebola and Zika Virus, although others, such as nutrition, cancer, fluoridation of water and smoking also featured. Studies adopted theoretical frameworks from psychology and network science, while co-citation analysis revealed potential for greater collaboration across fields. Most studies employed content analysis, social network analysis or experiments, drawing on disparate disciplinary paradigms. Future research should examine susceptibility of different sociodemographic groups to misinformation and understand the role of belief systems on the intention to spread misinformation. Further interdisciplinary research is also warranted to identify effective and tailored interventions to counter the spread of health-related misinformation online.

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Section: Resultssupporting

confidence: 80%

Section: Vaccines and Communicable Diseasesmentioning

confidence: 99%

Systematic Literature Review on the Spread of Health-related Misinformation on Social Media

Wang

McKee

Torbica

et al. 2019

Social Science & Medicine

1,243

806

View full text Add to dashboard Cite

show abstract

“…Huang et al [9] found a high positive correlation between reported influenza attitude and behavior on Twitter and influenza vaccination coverage in the US. In contrast, Aquino et al [10] found an inverse correlation between Mumps, Measles, Rubella (MMR) vaccination coverage and tweets, Facebook posts and internet search activity about autism and MMR vaccine in Italy. This outcome was possibly due to a decision of the Court of Justice in one of the regions to award vaccine-injury compensation for a case of autism.…”

Section: Introductionmentioning

confidence: 92%

Monitoring stance towards vaccination in twitter messages

Kunneman

Lambooij

Wong

et al. 2020

BMC Med Inform Decis Mak

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Background: We developed a system to automatically classify stance towards vaccination in Twitter messages, with a focus on messages with a negative stance. Such a system makes it possible to monitor the ongoing stream of messages on social media, offering actionable insights into public hesitance with respect to vaccination. For Dutch Twitter messages that mention vaccination-related key terms, we annotated their stance and feeling in relation to vaccination (provided that they referred to this topic). Subsequently, we used these coded data to train and test different machine learning set-ups. With the aim to best identify messages with a negative stance towards vaccination, we compared set-ups at an increasing dataset size and decreasing reliability, at an increasing number of categories to distinguish, and with different classification algorithms. Results: We found that Support Vector Machines trained on a combination of strictly and laxly labeled data with a more fine-grained labeling yielded the best result, at an F1-score of 0.36 and an Area under the ROC curve of 0.66, outperforming a rule-based sentiment analysis baseline that yielded an F1-score of 0.25 and an Area under the ROC curve of 0.57. Conclusion: The outcomes of our study indicate that stance prediction by a computerized system only is a challenging task. Our analysis of the data and behavior of our system suggests that an approach is needed in which the use of a larger training dataset is combined with a setting in which a human-in-the-loop provides the system with feedback on its predictions.

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“…The spread of fake news and misinformation on social media is blamed as a primary cause of vaccine hesitancy (Aquino et al, ; Dube, Vivion, & MacDonald, ; Jolley & Douglas, ; Smith & Marshall, ). This originated from the measles‐mumps‐rubella (MMR)—autism controversy that stemmed from Andrew Wakefield's fake study.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with pre‐2012, the volume of searches increased by 600%. Indeed, as also supported by the relevant medical literature such as Aquino et al () and Donzelli et al (), the court's ruling allows us to establish a crucial trigger for the virality of (fake) news and misinformation surrounding vaccines in Italy.…”

Section: Introductionmentioning

confidence: 99%

Vaccine hesitancy and (fake) news: Quasi‐experimental evidence from Italy

2019

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The spread of fake news and misinformation on social media is blamed as a primary cause of vaccine hesitancy, which is one of the major threats to global health, according to the World Health Organization. This paper studies the effect of the diffusion of misinformation on immunization rates in Italy by exploiting a quasi‐experiment that occurred in 2012, when the Court of Rimini officially recognized a causal link between the measles‐mumps‐rubella vaccine and autism and awarded injury compensation. To this end, we exploit the virality of misinformation following the 2012 Italian court's ruling, along with the intensity of exposure to nontraditional media driven by regional infrastructural differences in Internet broadband coverage. Using a Difference‐in‐Differences regression on regional panel data, we show that the spread of this news resulted in a decrease in child immunization rates for all types of vaccines.

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The web and public confidence in MMR vaccination in Italy

Cited by 123 publications

References 7 publications

Systematic Literature Review on the Spread of Health-related Misinformation on Social Media

Systematic Literature Review on the Spread of Health-related Misinformation on Social Media

Monitoring stance towards vaccination in twitter messages

Vaccine hesitancy and (fake) news: Quasi‐experimental evidence from Italy

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