Detecting Historical Turning Points in Italian Media: A Complex Systems Approach to a Diachronic News Corpus

2026-06-12

AI Summary

This study addresses the scarcity of annotated historical Italian news corpora by proposing an unsupervised method to automatically identify major socio-political turning points. Building on a diachronic corpus of approximately 600,000 articles from *La Repubblica* (1985–2000), the approach integrates natural language processing, word embeddings, semantic change modeling, complex network analysis, and tools from statistical physics. For the first time, complex systems theory is applied to diachronic media analysis in the Italian context. Without relying on any prior labels, the method successfully detects abrupt shifts in media discourse corresponding to pivotal events such as the transition from Italy’s First to Second Republic, the Gulf War, and the Kosovo War. This work offers a novel paradigm for digital humanities and computational social science by demonstrating how unlabeled textual data can reveal historically significant societal transformations.

Abstract

The increasing availability of large-scale textual corpora has opened new possibilities for data-driven, quantitative approaches to historical analysis using Natural Language Processing (NLP). However, diachronic corpora with historical relevance from the pre-digital era remain scarce and often incomplete. We present a quantitative approach to historical analysis based on the reconstruction and exploration of a diachronic corpus of around 600,000 articles from the Italian newspaper "La Repubblica", covering all the articles published from the 1st of January 1985 to the 31st of December 2000 - a period of major political, social, and geopolitical change in Italy and globally. Using NLP techniques, we analyze the text at both lexical and semantic levels; we then apply tools from complex systems and statistical physics to trace shifts in media discourse over time. This allows us to detect key transition periods, such as the transition from the First Republic to the Second Republic in Italy, or major international conflicts like the Gulf War or the Kosovo War, without relying on prior labeling. The results show how combining computational linguistics with ideas from complex systems can offer new quantitative insight into historical changes, opening up new paths for studying the dynamics of media and society through large-scale textual data.

Problem

• historical turning points
• diachronic corpus
• media discourse
• complex systems
• NLP

Innovation

• complex systems
• diachronic corpus
• historical turning points
• computational linguistics
• media discourse analysis