  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.
