DYNAMOD-VACCINE-DATA · A new method for dynamic opinion modelling of surveys applied to vaccine hesitancy data
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2020-06-01 → 2023-05-31
- EU contribution
- €294,886
- Participants
- 1
- Scheme
- MSCA-IF
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Results in brief
A new method for dynamic opinion modelling of surveys applied to vaccine hesitancy data
The project aimed to explore the pressing global social issue of the disruption of trust in scientific progress and technological solutions, such as climate change denial and especially vaccine skepticisms. This phenomenon, in 2019 has been recognized by the World Health Organization as one of the ten most serious threats to global health. The research project aimed at developing novel methods to explore patterns and temporal trends in real opinion systems, specifically related to vaccine opinions. While many predictive models already existed in the field of opinion dynamics, most of them were not yet applied to empirical data. The project aimed to bridge this gap by connecting state-of-the-art opinion dynamics models to survey data. The project reached its initial goal and much more. Indeed, it provided: - Exploration of the connection between measurements of opinions and opinion dynamics data - Validation of the micro-dynamics of opinion dynamics models - Exploration of opinions as a belief network - The connection of opinion dynamics models to vaccination data
Data: CORDIS, © European Union
Project objective
Vaccine hesitancy (delaying or refusing of vaccination) has been identified by the World Health Organization as one of the top-ten threats to global health. The spreading of vaccine-hesitancy in society is a complex phenomenon and no method can currently predict which countries will become vulnerable to this threat. Opinion dynamics models have enormous – as yet unrealised – potential to identify countries where vaccine-hesitant opinions are likely to spread or be resisted. They simulate the evolution of public opinion with computational models in which agents interact based on simple rules, with the goal of precisely modelling the spread of opinions in networks. However, while many successful theoretical models exist, few have been run on empirical data. This is because most models require detailed network information and are therefore not compatible with common data types (i.e. survey data). In this project, I will develop a novel method for reconstructing social network information from survey responses alone. First, the method will be validated using simulations. Then, it will be applied to secondary vaccine-hesitancy survey datasets to compare the predictive capability of different opinion dynamics models in this context. This study will provide two main outputs. First, a toolkit to identify societies most vulnerable to vaccine-hesitancy opinion spreading. Second, a method for inferring underlying social networks from survey data. This will have general value for research on any social issue related to opinion-coordination, e.g. climate change; GMOs etc. This fellowship will transfer my mathematical and computational modelling expertise to my hosts. At the same time, it will provide me with synergistic expertise in social science and network science as a platform for my research career in computational social science.
Original text from CORDIS.
Participants
- UNIVERSITY OF LIMERICK · LimerickCoordinatorIreland
Links
Data: CORDIS, © European Union