Scientists study anti-vaccine arguments to develop strategies for raising awareness about the importance of vaccination
Based on the identification of 11 types of attitude roots opposing vaccination, researchers have launched a free online tool that allows anti-vaccine arguments to be quickly recognised and, at the same time, constructively refuted.
English version: Diana Taborda
A research team conducted a study to find out why people believe misinformation about vaccines and identified 11 main psychological reasons (so-called attitude roots) that explain why an individual might express opposition to vaccination. With this analysis, scientists aim to help combat misinformation more effectively.
After identifying and validating these 11 root attitudes, the team launched a free web-based tool that allows to swiftly identify anti-vaccine arguments and then outline the rebuttal of these arguments in a positive way. The online platform provides over 60 misinformation themes that may arise in professional or personal face-to-face conversations between doctors and patients, colleagues or friends. The research work thus aims to pave the way for the development of further refutation strategies and other interventions for more tailored responses to anti-vaccination arguments.
In the scientific article "A taxonomy of anti-vaccination arguments from a systematic literature review and text modelling", published today in the journal Nature Human Behaviour, the research team aimed to identify "the psychological attributes that drive a person to side with vaccination opposition", explains Angelo Fasce, researcher at the Faculty of Medicine of the University of Coimbra (FMUC) and first author of the study.
Fasce adds that "The threat of anti-vaccination arguments requires an approach that goes beyond addressing flaws in the arguments, by also considering the attitude roots—that is, the underlying psychological attributes driving a person’s belief—of opposition to vaccines". In the scope of this research, attitudes are about the beliefs and ideas that people develop through social interaction and experiences. It is according to these attitudes that human beings respond positively or negatively to people, situations or objects. “Vaccine hesitancy is a concept that encompasses a spectrum of attitudes, from the refusal of all vaccines to accepting vaccination despite uncertainties about doing so, thus posing a barrier to achieving sufficiently high vaccine uptake to protect all communities”.
The lead author claims that “One of the most promising approaches to overcome vaccine hesitancy is the face-to-face dialogue between patients and healthcare professionals, so we tried to find opposition to vaccination patterns in order to provide these professionals with communication strategies that are effective when dealing with vaccine-hesitant individuals, who may be influenced by deliberate misinformation about vaccines. Some of these root attitudes of opposition are becoming increasingly common in consultations and require specific training to be handled properly".
The authors identified 11 root attitudes: conspiracist ideation; distrust; unwarranted beliefs; worldview and politics; religious concerns; moral concerns; fear and phobias; distorted risk perception; perceived self-interest; epistemic relativism; reactance
“This taxonomy of arguments is a useful resource for health professionals as it provides appropriate rebuttals for each argument and a skilful response that can be used when patients put forth a particular anti-vaccination argument”, says Angelo Fasce. “The tool was developed “in collaboration with healthcare professionals from the UK, and allows practitioners to identify the root underlying a patient’s contrarian argument and then outline how it might best be rebutted without directly challenging the patient’s underlying attitude”.
The research team validated the taxonomy through a combination of human coding and machine learning using natural language processing algorithms. They conducted a systematic review of previous scientific work on anti-vaccine arguments, and fact-checked misinformation related to vaccination against COVID-19. “The text-classification results obtained suggest that we can leverage academic knowledge in the form of scientific literature to refine artificial intelligence models, enabling them to more easily identify attitude roots present in the kind of text one might come across in a more informal context”.
This scientific article was developed in the framework of the European project "JITSUVAX. Jiu-jitsu with misinformation in the age of COVID: Using refutation-based learning to enhance vaccine uptake and knowledge among healthcare professionals and the public", funded with over 3 million euros by the European Commission. Stephan Lewandowsky, a professor at the University of Bristol (UK) is the Principal Investigator (PI), and Fernanda Rodrigues, professor at the Faculty of Medicine of the University of Coimbra, leads the project in Portugal.
JITSUVAX aims to analyse and debunk misinformation and scepticism about the efficacy and safety of vaccines, in order to provide both healthcare professionals and citizens with more effective and positive communication skills, and thus increase public confidence in vaccination for the sake of public health.
The PI of the project explains that “misinformation about vaccinations has serious adverse consequences for society. For example, a recent Canadian report estimated the cost of misinformation to be around $300 million, with nearly 3,000 additional deaths arising from people’s reluctance to get vaccinated because they were misinformed. Understanding how people can be protected against being misled is therefore crucial to safeguard public health. The EU Horizon-funded JITSUVAX project has developed tools to understand and correct anti-vaccination misinformation».
The scientific article “A taxonomy of anti-vaccination arguments from a systematic literature review and text modelling” is available here.
More information about the “JITSUVAX” project can be found at https://jitsuvax.info/.