# Topic modelling discourse dynamics in historical newspapers

arXiv: https://arxiv.org/abs/2011.10428 · HTML: https://mlsanity.com/paper/2011.10428/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of COVID-19 in Bangladesh](https://mlsanity.com/paper/2101.09667/) (2021)
- [Understanding Archives: Towards New Research Interfaces Relying on the Semantic Annotation of Documents](https://mlsanity.com/paper/2403.19201/) (2024)

## Abstract (arXiv)

This paper addresses methodological issues in diachronic data analysis for historical research. We apply two families of topic models (LDA and DTM) on a relatively large set of historical newspapers, with the aim of capturing and understanding discourse dynamics. Our case study focuses on newspapers and periodicals published in Finland between 1854 and 1917, but our method can easily be transposed to any diachronic data. Our main contributions are a) a combined sampling, training and inference procedure for applying topic models to huge and imbalanced diachronic text collections; b) a discussion on the differences between two topic models for this type of data; c) quantifying topic prominence for a period and thus a generalization of document-wise topic assignment to a discourse level; and d) a discussion of the role of humanistic interpretation with regard to analysing discourse dynamics through topic models.