Modelling Societal Knowledge in the Health Sector: Machine Learning and Google Trends

The task for cognitive scientists has recently become the development of computable models that can replicate the process of human cognition, both at the individual and at the aggregate level. We present a computational model of the social cognitive processes related to the acquisition of new knowledge in the medical sector; that is, of the emerging associations between health-related concepts. Under the theoretical framework of connectionism and social cognition, we propose a method for modeling the conceptual system related to medical knowledge held by a society, on the basis of Internet search queries produced by it over time. Our model can be used to simulate the learning about medical issues by a society through language, and this has implications for the early detection of pandemics and the identification of appropriate responses by means of public information campaigns. We suggest how to use this model in connection with the COVID-19 pandemic. JEL code: I12