Navigating the Mise-en-Page: Interpretive Machine Learning Approaches to the Visual Layouts of Multi-Ethnic Periodicals

TL;DR

This paper introduces a machine learning-based computational approach to analyze and interpret the visual layouts of multi-ethnic newspapers from late 19th and early 20th century America, revealing patterns of editorial expression.

Contribution

It combines MARC data and machine learning datasets to analyze high-dimensional visual similarities in newspaper layouts, offering a novel method beyond content-focused approaches.

Findings

Identifies visual layout patterns across multi-ethnic newspapers.

Provides insights into editorial communication through layout analysis.

Enhances understanding of historical newspaper design and protest.

Abstract

This paper presents a computational method of analysis that draws from machine learning, library science, and literary studies to map the visual layouts of multi-ethnic newspapers from the late 19th and early 20th century United States. This work departs from prior approaches to newspapers that focus on individual pieces of textual and visual content. Our method combines Chronicling America's MARC data and the Newspaper Navigator machine learning dataset to identify the visual patterns of newspaper page layouts. By analyzing high-dimensional visual similarity, we aim to better understand how editors spoke and protested through the layout of their papers.