Data Science and the Dynamics of Collective Memory - CANDIA VALLEJOS, CRISTIAN ESTEBAN (Oxford University PressNew York, 2025)
Abstract This chapter introduces computational social science (CSS), which provides a data-driven, interdisciplinary framework for studying the formation, persistence, and decay of collective memory by integrating digital archives, social networks, and large-scale attention data. CSS enables researchers to quantify how collective memory evolves, shaping historical narratives and retention. A key result is the bi-exponential decay model, which shows that collective memory fades in two stages: a rapid initial drop as events lose relevance, followed by a slower decline as certain memories become culturally embedded. Collective attention serves as a measurable proxy for these shifts, enabling researchers to track public interest through Wikipedia views, scientific citations, media coverage, and other large-scale data sources. By bridging social memory theories with computational methods, CSS provides new insights into how collective attention shapes the long-term dynamics of memory in the digital age.