Data Science and the Dynamics of Collective Memory
Abstract
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.
Type
Publication
In Cognition, Culture, and Political Momentum: Breaking down the Silos in Collective Memory Research, Oxford University Press
Cristian Candia
Associate Professor and Head of CRiSS-LAB, School of Engineering and School of Government, Universidad del Desarrollo, Chile.
My research interests include applied AI, computational social science, network science, collective intelligence, school coexistence, decision intelligence, and business analytics.