Data Science and the Dynamics of Collective Memory
Resumen
This chapter introduces computational social science as a data-driven framework for studying how collective memory forms, persists, and decays. It connects digital archives, social networks, large-scale attention data, and bi-exponential decay models to explain how communicative and cultural memory shape long-term attention.
Publicación
In Cognition, Culture, and Political Momentum: Breaking down the Silos in Collective Memory Research, Oxford University Press
Cristian Candia
Profesor Asociado y Director de CRiSS-LAB, Facultad de Ingeniería y Facultad de Gobierno, Universidad del Desarrollo, Chile.
Mis intereses de investigación incluyen IA aplicada, ciencias sociales computacionales, ciencia de redes, inteligencia colectiva, convivencia escolar, inteligencia de decisiones y analítica de negocios.