Following Socio-Environmental Conflict Narratives About Energy Transition in Chile: A Spatio-Temporal Analysis Using Dynamic Topic Modeling

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:A robust spatio-temporal topic modeling approach (RollingLDA) for analyzing large-scale socio-environmental conflict narratives, with reproducible code.

🏢実務担当者:The interactive dashboard and topic mapping can help organizations monitor and anticipate energy transition related public concerns.

🏛政策担当者:Insights into how narratives evolve can guide communication strategies and conflict resolution in energy transition planning.

Understanding the construction of socio-environmental narratives at a national scale is a complex challenge, particularly when research remains fragmented across disconnected case studies. In Chile, the energy transition has generated territorial disputes as extractive industries and renewable energy projects expand, yet large-scale systematic analyses of how these conflicts are represented in public discourse remain scarce. This paper addresses this gap by applying a spatio-temporal topic modelling framework to a corpus of 1,996 validated news articles covering conflicts related to the energy transition in Chile from 2011 to 2025. Using RollingLDA, a dynamic adaptation of latent Dirichlet allocation that prevents information leakage from future documents, we identify twelve topics that provide insights into the public narratives surrounding socio-environmental conflicts. Our analysis reveals how specific conflicts, such as the HidroAysén dam project, the Dominga mining controversy, and pollution in sacrifice zones such as Quintero-Puchuncaví, have evolved over time, with some narratives declining while others, including green hydrogen development and lithium extraction, have emerged as central concerns. We complement this temporal analysis with a spatial dimension by mapping the prevalence of topics across Chilean regions through an interactive dashboard. By combining established methods, our work offers a reproducible framework that can be adapted to topic modelling results incorporating spatial and temporal dimensions, enabling the tracking of how socio-environmental narratives emerge, evolve, and fade over time. Please also refer to the GitHub repository at https://github.com/JonasRieger/t2s2026.

• openalex https://doi.org/10.31235/osf.io/xqn3f_v1first seen 2026-07-08 05:01:01