Classifying Textual Genre in Historical Magazines (1875-1990) (2025)

Abstract: Historical magazines are a valuable resource for understanding the past, offering insights into everyday life, culture, and evolving social attitudes. They often feature diverse layouts and genres. Short stories, guides, announcements, and promotions can all appear side by side on the same page. Without grouping these documents by genre, term counts and topic models may lead to incorrect interpretations.This study takes a step towards addressing this issue by focusing on genre classification within a digitized collection of European medical magazines in Swedish and German. We explore 2 scenarios: 1) leveraging the available web genre datasets for zero-shot genre prediction, 2) semi-supervised learning over the few-shot setup. This paper offers the first experimental insights in this direction.We find that 1) with a custom genre scheme tailored to historical dataset characteristics it is possible to effectively utilize categories from web genre datasets for cross-domain and cross-lingual zero-shot prediction, 2) semi-supervised training gives considerable advantages over few-shot for all models, particularly for the historical multilingual BERT.

## introduction quantitative processing of digitized archives referred to as "distant reading" helps historians in conducting large-scale analysis and categorization of their data @xcite . however, it is of utmost importance to develop methods that can contribute to reliable interpretations @xcite . this paper proposes genre foot_1 classification to improve reliability of distant reading interpretations for visually-and information-rich materials, such as historical magazines. the actdisease foot_2 dataset is an extensive private collection currently being digitized as part of an ongoing project on the modern history of european medicine. it consists of medical magazines issued by ten european patient organizations in four languages (swedish, german, french, and english) throughout the 20th century. each magazine had a different publication frequency, resulting in a varying number of issues per year and featuring diverse page formats and visually complex layouts. within the same page, these magazines often combine texts that carry different communicative purposes, such as personal narratives, advertisements, instructions, short stories, etc. failing to group these texts hinders the accurate interpretation of term counts and topic models across historical periods, as results may be skewed toward the most frequent genres. moreover, grouping by genre enriches historical interpretations by providing a broader view of evolving communicative strategies over time @xcite and allowing for more finegrained analyses of term distributions and topic models. in the historical research, these text groups are referred to as "epistemic genres" and have been recognized as a valuable conceptual tool for exploring cross-cultural history of medicine @xcite . their use is linked to the development of knowledge communities, such as patient organizations in our case. due to scarcity of annotated data for our dataset, this paper explores the effectiveness of zero-shot learning using publicly available datasets annotated with web genres and registers, and assesses the mapping of the existing categories to our custom ones. we also investigate the impact of few-shot learning, comparing the standard approach with a semi-supervised method that incorporates prior fine-tuning on the full dataset to leverage broad knowledge and task-specific adaptation. additionally, we compare the performance of models pretrained on modern versus historical data in classifying genres within historical materials. the paper is organized as follows. section 2 discusses work on genre classification for historical materials, as well as the recent advances in genre classification with llms. section 3 provides definitions for genre categories. section 4 gives details on the datasets used for zero-shot and few-shot experiments. section 5 outlines the experimental setup: models and types of experiments. section 6 discusses the results, and section 7 concludes the paper. ## related work the application of classical machine learning (ml) methods to genre extraction from historical newspapers has been discussed in @xcite . rather than proposing a specific algorithmic solution, the authors focus on the challenges of defining and annotating genres in historical newspapers, as well as the difficulties in transparently evaluating and comparing different algorithms. while automatic genre classification for historical sources remains relatively unexplored, there is extensive research on automatic identification of web genres and registers @xcite . the state-of-the-art textual genre classifier is a version of xlm-roberta @xcite fine-tuned on a combination of web genre datasets with extensive genre category coverage @xcite . we extend this work by comparing the performance of three multilingual encoders -xlm-roberta, mbert @xcite and historical mbert @xcite -for zero-shot and few-shot genre classification of multilingual historical magazines. ## genre categories a set of genre categories was defined under the supervision of the main historian of the project who specializes in patient organizations. academic reports about academic research or explains complex scientific ideas in an accessible way (research article, report or popular science article). administrative reports about the activities or operations of the patient organizations (meeting minutes, financial reports, annual reports, editorial information, official correspondence and petitions, announcements). advertisement promotes products or services with intent to sell them (promotion, advertisement). guide provides advice or instructions for step-by-step implementation to achieve a certain goal or solve a problem related to health, legal issues or other (dietary advice, physical exercise instructions, recipe, procedural instructions, application guidelines). fiction aims to entertain the reader, gives reading pleasure, engages the reader emotionally (poems, short stories, humor, myths, novel, novellas). legal explains or informs about terms and conditions (contract, rules, amendment, general terms and conditions). news informs or reports about updates on recent events and important developments (daily news). nonfiction prose (nf_prose) narrates about events or experiences from personal life or represents a description of cultural phenomena or history (historical narrative, auto(biography), memoire, travel note, personal letter, opinion essay, cultural article, documentary prose). qa is text structured in a question-answer format, for example questions from members and answers from medical professionals. ## datasets for zero-shot prediction, we take advantage of the publicly available datasets used in the previous work for automatic genre identification (agi) @xcite and the investigation of cross-lingual genre transfer in dependency parsing @xcite . the entire actdisease dataset is used to finetune the models for masked language modeling (mlm) in the semi-supervised few-shot scenario. a portion of it is annotated for the experiments. ## experimental setup the main goal of this study is to identify the optimal training dataset configurations and fine-tuning strategies for aligning with the annotated genre labels. we explore two scenarios: zero-shot prediction, where models predict genres using existing web genre datasets without seeing the target data, and few-shot vs. semi-supervised few-shot training. in the semi-supervised scenario, we examine if pre-training the models for mlm on the entire act-disease dataset improves their few-shot prediction performance. ## conclusions in this work, we address an underexplored problem of genre classification for historical magazines. first, we show that with a custom genre scheme based on the dataset properties it is possible to successfully leverage the categories available in the modern datasets in cross-domain and crosslingual zero-shot prediction. our analysis reveals that models trained on the ftd dataset configurations achieve better alignment with our genre mapping compared to those trained on other datasets. next, we highlight the advantages of few-shot learning using a small set of annotated instances. even a limited annotated sample from the same data source greatly enhances genre classification performance on our historical test dataset. furthermore, we find that prior mlm fine-tuning substantially improves few-shot learning across all models, with particularly strong gains for historical multilingual bert. for future work, we aim to expand annotation efforts to include new genre categories and languages (english and french). once sufficient annotations are available, we will also explore monolingual few-shot experiments to compare the performance of monolingual and multilingual large language models on this task. in addition, we plan to investigate how linguistic similarities between training and test genre data are related to the classification performance.