Abstract This study integrates multilingual text analytics with time-series modeling to examine how geopolitical narratives emerging around the 2022 Russia–Ukraine conflict align across Russian and Colombian media, drawing on a corpus spanning 2013–2023 that also encompasses the 2014 conflict period. Drawing on a corpus of more than 38, 000 Spanish- and Russian-language news articles (2013–2023), we employ a six-stage pipeline combining multilingual NER, document-level sentiment scoring, e5-large-instruct embeddings, BERTopic clustering, and a semi-automated human-LLM labeling workflow. Narrative salience and tone were aggregated to weekly, log-normalized series and analyzed using linear regression and Granger causality tests (lags 1–4 weeks), providing a descriptive view of temporal coupling rather than evidence of direct causal influence. The period surrounding the 2022 invasion produced the clearest and most coherent patterns. Across four macro-narratives (Security & Conflict, Diplomacy, Economy, and Politics & Society), Russian and Colombian coverage exhibited sequential alignment in which shifts in narrative volume were often followed by shifts in evaluative tone. Thirty-two of forty-four target–topic pairs displayed significant lag structures prior to false-discovery correction (p 0. 05 p<0. 05 p less than 0. 05), with 25 surviving Benjamini–Hochberg adjustment. Categories with lower geopolitical salience (Technology, Health, and Off-topic) displayed less consistent temporal coupling, though this contrast is offered as descriptive context rather than a formal control. While these results do not imply coordinated messaging, they indicate recurring forms of narrative mimicry, understood here as patterned convergence in framing and sentiment across media ecosystems. By demonstrating how transformer-based semantic representations can be integrated with human-in-the-loop interpretation and classical time-series analysis, this study contributes a reproducible workflow for tracing narrative trajectories at scale. The approach provides a methodological foundation for examining how geopolitical frames circulate, mutate, and align across linguistic and regional boundaries, offering new possibilities for computational humanities research on transnational discourse.
Parson et al. (Thu,) studied this question.