Staying at home - the interplay between behavioural synchronisation and physical distancing in prosocial behaviour
The COVID-19 outbreak is a public health and economic crisis, unprecedented in human history and as the epidemic progresses, it becomes obvious that human behaviour plays a crucial role in curbing the epidemic spread. In liberal democracies, governments largely rely on the population’s willingness to adhere to measures. Adherence to measures is framed as a prosocial act but the consequence - staying at home - isolates individuals from the collective and counteracts behavioural synchronization. This leads to competing effects on the levels of prosociality in a population. Understanding these dynamics is of great importance to evaluate the sustainability of measures but to date, there is no assessment of the influence on prosocial behaviour on the level of adherence to measures. To this end, I will numerically model prosociality in a population during a pandemic as a dynamical system. Here, prosociality is subject to a driving force (severity of pandemic), positive feedback through emotional synchronization (news, social media) and dampening (quarantine fatigue). To parameterize the model I will measure collective and individual levels of prosociality in a population using digital traces on social media platforms. By applying the LIWC method on, for example, a corpus collected from Twitter for different countries in the period before and during the pandemic, population levels of prosociality can be extracted. I will use the parameterized model to compare different liberal democracies and assess the combined impact of prosociality and non-pharmaceutical intervention measures on the prevention of the spread of COVID-19. To this end, I have established collaborations with eminent epidemiologists. Furthermore, I will implement a public monitor for prosociality (and other emotions such as anger) for European countries that will enable decision-makers to assess public sentiment in a timely and quantitative manner.