DCODES

Background

The COVID-19 pandemic has been followed by a massive infodemic, defined as an over-abundance of information some accurate and some not that makes it hard for people to find trustworthy sources and reliable guidance when they need it. In these days, the propagation of health misinformation through social media has become a major public health concern. Although today there is broad agreement among health professionals, researchers and policy makers on the need to control health misinformation, there is still little evidence about the effects that the dissemination of false or misleading health messages through social media could have on public health in the near future. Despite recent studies which are exploring innovative ways to effectively combat health misinformation online, additional research is needed to characterise and capture this complex social phenomenon. The DCODES project aims to study the emergence and dynamic of the COVID-19 infodemic and its impact on social and health decision-making processes. In particular, we want to know how waves of unreliable and poor quality information might potentially impacts on society’s capacity to respond adaptively to new health emergencies by rapidly adopting new opinions, norms, and behaviors that may effectively determine the propagation of the pandemic. The achievement of the research objectives requires the combination of multi/inter-disciplinary research strategies that allow us to study health misinformation from different research perspectives. The DCODES project combines methods from the field of Computational Social Science (social media mining, social network analysis, machine learning, and social simulations) to provide new answer that help us to control and manage future pandemics and infodemics.