Navigating the Mise-en-Page: Interpretive Machine Learning Approaches to the Visual Layouts of Multi-Ethnic Periodicals
This work addresses a gap in historical and literary studies by shifting focus from individual content to visual layouts, though it is incremental as it builds on existing datasets and methods.
The paper tackles the problem of analyzing visual layouts in multi-ethnic historical newspapers by developing a computational method that combines MARC data and the Newspaper Navigator dataset to map high-dimensional visual similarities, aiming to understand editorial communication through layout patterns.
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.