Description du projet
Suivi de la prosocialité pendant une pandémie
Pour limiter la propagation du coronavirus, les gens étaient tenus de se distancer socialement. C’est l’une des principales stratégies de contrôle des infections non pharmaceutiques imposées. Le succès dépend du comportement humain, en particulier de la volonté des gens d’adhérer aux mesures. Dans ce contexte, le projet STAY, financé par l’UE, étudiera le comportement prosocial lié au niveau de conformité. Plus précisément, il mesurera les niveaux collectifs et individuels de comportement prosocial à l’aide de traces numériques sur les plateformes de réseaux sociaux. Il appliquera le programme d’analyse de texte Linguistic Inquiry and Word Count (LIWC) pour examiner un corpus collecté sur Twitter pour différents pays au cours de la période avant et pendant la pandémie. Faisant la lumière sur les niveaux de prosocialité de la population, les résultats seront utiles aux décideurs politiques.
Objectif
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.
Champ scientifique
- humanitieshistory and archaeologyhistory
- medical and health scienceshealth sciencespublic healthepidemiologypandemics
- natural sciencesmathematicsapplied mathematicsdynamical systems
- social sciencespolitical sciencesgovernment systemsdemocracy
- medical and health scienceshealth sciencesinfectious diseasesRNA virusescoronaviruses
Programme(s)
Régime de financement
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinateur
8010 Graz
Autriche