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This paper presents a computational method of analysis that draws frommachine learning, library science, and literary studies to map the visuallayouts of multi-ethnic newspapers from the late 19th and early 20th centuryUnited States. This work departs from prior approaches to newspapers that focuson individual pieces of textual and visual content. Our method combinesChronicling America's MARC data and the Newspaper Navigator machine learningdataset to identify the visual patterns of newspaper page layouts. By analyzinghigh-dimensional visual similarity, we aim to better understand how editorsspoke and protested through the layout of their papers.

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