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  4. Data Science and the Dynamics of Collective Memory
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Data Science and the Dynamics of Collective Memory

Journal
Cognition, Culture, and Political Momentum
Date Issued
2025-11-23
Author(s)
CANDIA VALLEJOS, CRISTIAN ESTEBAN  
Facultad de Ingeniería  
Type
text::book::book part
DOI
10.1093/oso/9780197788332.003.0021
URL
https://hdl.handle.net/123456789/12383
Abstract
<jats:title>Abstract</jats:title>
<jats:p>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.</jats:p>
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