Approaches to Analysing Historical Newspapers Using LLMs

• URL: http://arxiv.org/abs/2603.25051v2
• Date: Fri, 27 Mar 2026 09:09:00 GMT
• Title: Approaches to Analysing Historical Newspapers Using LLMs
• Abstract summary: This study presents a computational analysis of the Slovene historical newspapers textitSlovenec and textitSlovenski narod from the sPeriodika corpus.<n>We examine how collective identities, political orientations, and national belonging were represented in public discourse at the turn of the twentieth century.
• Score: 1.0225484904475783
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study presents a computational analysis of the Slovene historical newspapers \textit{Slovenec} and \textit{Slovenski narod} from the sPeriodika corpus, combining topic modelling, large language model (LLM)-based aspect-level sentiment analysis, entity-graph visualisation, and qualitative discourse analysis to examine how collective identities, political orientations, and national belonging were represented in public discourse at the turn of the twentieth century. Using BERTopic, we identify major thematic patterns and show both shared concerns and clear ideological differences between the two newspapers, reflecting their conservative-Catholic and liberal-progressive orientations. We further evaluate four instruction-following LLMs for targeted sentiment classification in OCR-degraded historical Slovene and select the Slovene-adapted GaMS3-12B-Instruct model as the most suitable for large-scale application, while also documenting important limitations, particularly its stronger performance on neutral sentiment than on positive or negative sentiment. Applied at dataset scale, the model reveals meaningful variation in the portrayal of collective identities, with some groups appearing predominantly in neutral descriptive contexts and others more often in evaluative or conflict-related discourse. We then create NER graphs to explore the relationships between collective identities and places. We apply a mixed methods approach to analyse the named entity graphs, combining quantitative network analysis with critical discourse analysis. The investigation focuses on the emergence and development of intertwined historical political and socionomic identities. Overall, the study demonstrates the value of combining scalable computational methods with critical interpretation to support digital humanities research on noisy historical newspaper data.

Related papers

• Triangulating Temporal Dynamics in Multilingual Swiss Online News [0.0]
  This paper studies temporal trends in Swiss digital media across the country's three main linguistic regions, French, German, and Italian.<n>We collected and processed over 1.7 million news articles, applying lexical metrics, named entity recognition and Wikidata-based linking, targeted sentiment analysis, and consensus-based change-point detection.<n>The findings reveal distinct temporal patterns and highlight how linguistic and cultural contexts influence reporting.
  arXiv  Detail & Related papers  (2026-03-23T03:27:00Z)
• Evaluating LLMs for Demographic-Targeted Social Bias Detection: A Comprehensive Benchmark Study [1.6682715542079583]
  Large-scale web-scraped text corpora used to train general-purpose AI models often contain harmful demographic-targeted social biases.<n>We present a comprehensive evaluation framework aimed at English texts to assess the ability of LLMs in detecting demographic-targeted social biases.<n>We then conduct a systematic evaluation with models across scales and techniques, including prompting, in-context learning, and fine-tuning.
  arXiv  Detail & Related papers  (2025-10-06T09:45:32Z)
• Interpreting Social Bias in LVLMs via Information Flow Analysis and Multi-Round Dialogue Evaluation [1.7997395646080083]
  Large Vision Language Models (LVLMs) have achieved remarkable progress in multimodal tasks, yet they also exhibit notable social biases.<n>We propose an explanatory framework that combines information flow analysis with multi-round dialogue evaluation.<n>Experiments reveal that LVLMs exhibit systematic disparities in information usage when processing images of different demographic groups.
  arXiv  Detail & Related papers  (2025-05-27T12:28:44Z)
• AgoraSpeech: A multi-annotated comprehensive dataset of political discourse through the lens of humans and AI [1.3060410279656598]
  AgoraSpeech is a meticulously curated, high-quality dataset of 171 political speeches from six parties during the Greek national elections in 2023.<n>The dataset includes annotations (per paragraph) for six natural language processing (NLP) tasks: text classification, topic identification, sentiment analysis, named entity recognition, polarization and populism detection.
  arXiv  Detail & Related papers  (2025-01-09T18:17:59Z)
• Beyond Partisan Leaning: A Comparative Analysis of Political Bias in Large Language Models [6.549047699071195]
  This study adopts a persona-free, topic-specific approach to evaluate political behavior in large language models.<n>We analyze responses from 43 large language models developed in the U.S., Europe, China, and the Middle East.<n>Findings show most models lean center-left or left ideologically and vary in their nonpartisan engagement patterns.
  arXiv  Detail & Related papers  (2024-12-21T19:42:40Z)
• Interactive Topic Models with Optimal Transport [75.26555710661908]
  We present EdTM, as an approach for label name supervised topic modeling. EdTM models topic modeling as an assignment problem while leveraging LM/LLM based document-topic affinities.
  arXiv  Detail & Related papers  (2024-06-28T13:57:27Z)
• Bias and Fairness in Large Language Models: A Survey [73.87651986156006]
  We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs) We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing. We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
  arXiv  Detail & Related papers  (2023-09-02T00:32:55Z)
• Fairness meets Cross-Domain Learning: a new perspective on Models and Metrics [80.07271410743806]
  We study the relationship between cross-domain learning (CD) and model fairness. We introduce a benchmark on face and medical images spanning several demographic groups as well as classification and localization tasks. Our study covers 14 CD approaches alongside three state-of-the-art fairness algorithms and shows how the former can outperform the latter.
  arXiv  Detail & Related papers  (2023-03-25T09:34:05Z)
• O-Dang! The Ontology of Dangerous Speech Messages [53.15616413153125]
  We present O-Dang!: The Ontology of Dangerous Speech Messages, a systematic and interoperable Knowledge Graph (KG) O-Dang! is designed to gather and organize Italian datasets into a structured KG, according to the principles shared within the Linguistic Linked Open Data community. It provides a model for encoding both gold standard and single-annotator labels in the KG.
  arXiv  Detail & Related papers  (2022-07-13T11:50:05Z)
• Author Clustering and Topic Estimation for Short Texts [69.54017251622211]
  We propose a novel model that expands on the Latent Dirichlet Allocation by modeling strong dependence among the words in the same document. We also simultaneously cluster users, removing the need for post-hoc cluster estimation. Our method performs as well as -- or better -- than traditional approaches to problems arising in short text.
  arXiv  Detail & Related papers  (2021-06-15T20:55:55Z)
• Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding [71.2260967797055]
  We propose a weakly-supervised approach for aspect-based sentiment analysis. We learn sentiment, aspect> joint topic embeddings in the word embedding space. We then use neural models to generalize the word-level discriminative information.
  arXiv  Detail & Related papers  (2020-10-13T21:33:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.