EU - STAY - Staying at home - the interplay between behavioural synchronisation and physical distancing in prosocial behaviour

Project: Research project

Project Details

Description

The COVID-19 pandemic is a public health crisis of unprecedented scale in modern times. Social and behavioural measures are important tools to curb the spread of the pandemic. The unfolding of the epidemic in a given country greatly depends on its implemented measures and the population’s willingness to adhere to them. Almost all countries have enforced or at least recommended some kind of physical distancing alongside wearing masks and washing hands, but it is unclear to what extent the population is following these policies. Additionally, during the last months, many countries have experienced movements that discourage adherence to measures and large protests against the measures. Human behaviour is, therefore, a large source of uncertainty in epidemic modelling and its understanding it is crucial to understand and prevent the spread of the virus in communities. Evidently, general pandemic modelling needs to be improved by including the modelling of human behaviour under the conditions of a pandemic. There is strong evidence that individual prosociality drives the willingness to adhere to measures and can be used as a proxy to assess this willingness. On the collective level, prosociality is subject to complex social and emotional interaction processes that are not yet understood in the context of an ongoing pandemic. The aim of this project is to understand how collective social and emotional interaction processes can sustain prosocial behaviour that prevents the spreading of COVID-19. To do so, I plan to combine large-scale data analysis of social media data with computational modelling of epidemic spreading and human behaviour. As a first step, I want to establish a correlation between an individual’s prosocial intentions, prosocial expressions on social media and increased willingness to adhere to measures through a large-scale survey of Twitter users. I then want to measure collective prosociality in a range of countries by large-scale data analysis of social media data. Subsequently, I want to test the hypothesis that collective prosociality levels in a country are (together with the timeline of country-specific countermeasures) predictive of (a) population mobility changes and (b) pandemic spread. Here, it is crucial to emphasise that collective prosociality can capture both mobility behaviour and cautionary behaviour, the latter of which is otherwise invisible and hard to assess. Lastly, I want to model collective prosociality as a dynamical system that is subject to reinforcing and dampening feedback by joint experiences and “measure fatigue”, respectively, and leads to collective levels of prosociality that oscillate in time.
StatusActive
Effective start/end date1/02/2231/01/24

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