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.
This paper presents a set of developing methods for analyzing Black, Latinx, and other ethnic newspapers in the late nineteenth and early twentieth-century United States. We address these newspapers in this essay because multi-ethnic newspapers faced challenges above and beyond their era’s respective forms of mainstream journalism. Contending with forces of racism and xenophobia often required editors to tailor their paper’s visual grammar, communicating in carefully guarded ways that were still capable of circulating to distant readers and communities. Those traditions of complex editorial craft in the multi-ethnic presses represent an especially rich archive for analysis.
Editors in the historical multi-ethnic press often chose to proceed circumspectly by focusing
on their newspaper’s ”mise-en-page.” Rather than publish bold condemnations in headlines or
editorials, it was frequently safer to rely on other afordances of newspapers provided by the
aggregate, often invisible techniques collapsed under the heading of editing. [
Computational methods make it possible to analyze the visual patterns of multi-ethnic newspapers at scale. While prior research has approached these questions through textual analysis, a pivot to computational visual analysis compensates for the frequently noisy OCR text produced from historically neglected multi-ethnic newspapers.
Our working method uses machine learning to map the layouts of hundreds of thousands of
newspaper pages, quantified by visual similarity, using the Newspaper Navigator dataset (as
pictured in Figure 1) [
In this paper, we describe work in progress that brings machine learning, library science, and literary studies into conversation. This work includes: (1) a dataset of visual features from 16.3 million pages of historical newspapers; (2) remediation in 207 catalog records for 309 ethnic editors; and (3) modes for reading the practical languages of editing in specific historical contexts. Prior research eforts blend together in our exploratory machine learning prototypes for quantitatively and qualitatively measuring the visual similarities of the formats of a newspaper page. These visual similarities can help illuminate the ideas and activism that flowed through the editorial craft and conduct of historical multi-ethnic newspapers. Moreover, our work formulating a “similarity score” affirms the promise of multi-disciplinary collaborations for improving access and analysis of larger-scale digitized newspaper collections.
Multiple fields are eager to explore the new possibilities for analyzing digitized collections of
historical newspapers. Complementary conversations in computational, archival, and literary
studies have begun to pursue those possibilities in rich but largely disparate ways. Silos persist
even as many scholars make parallel use of the Library of Congress’s Chronicling America and
other national newspaper digital initiatives[
This paper builds on multi-ethnic press scholarship and archival research in Chronicling
America and other repositories. The prevailing view of political activism in historical
multiethnic newspapers has tended to focus on the textual and visual content that drove demands
for social justice [
Conversations in library and archival studies are exploring how best to account for histories
of race and ethnicity in metadata. Rather than being reduced to a theme or topic within a
collection, specialists have rethought how race and ethnicity might work as organizing
principles for collecting and describing historical materials. Race and ethnicity as categories of
identity are historically contingent and socially constructed. That fluidity challenges the
rigors of knowledge organization systems that impose stable categories and schemas. For some
archivists, however, categories of race and identity productively serve as a form of provenance
to guide collection development and access policies [
At the same time, an overabundance of work with more “computationally robust,”
institutionally privileged white print collections has prompted preliminary conversations
regarding the directions of computational humanities research. Indeed, from OCR engines that
do not support indigenous languages or diferent dialects, to entire subfields of natural
language processing and textual analysis that assume English as the default language of study,
algorithmically-mediated erasure continues to be uncovered and detailed by scholars [
Computational scholars have isolated visual content within periodicals in order to produce datasets of isolated content. Humanistic scholars have utilized these atomized items for interpretation. We are instead interested in the visual layouts themselves and the relations they imply on the page. By treating relations and patterns within layouts as forms of interpretive content – reading them as a “visual grammar” of photographs, advertisements, headlines, and beyond – we are in efect exploring a publication’s creative history, local contexts, and circulation networks.
The work described in this paper began in part with the Newspaper Navigator project,
created as part of Library of Congress’s Innovator in Residence program in 2019 with the goal
of excavating visual content in Chronicling America. Developed in partnership with LC Labs,
the National Digital Newspaper Program, and IT Design & Development at the Library of
Congress, Newspaper Navigator utilizes a machine learning model to extract seven visual
features from 16 million pages in Chronicling America: advertisements, photographs, illustrations,
maps, comics, editorial cartoons, and headlines [
Newspaper Navigator coincides with current trends in periodical studies. As Sean Latham
and Robert Scholes argue in an influential essay, “We have often been too quick to see magazines
[and other periodicals] as containers of discrete bits of information rather than autonomous
objects of study” [
We saw the possibility of combining these two types of reading. On one hand, Newspaper Navigator makes it possible to chart visual features across the entirety of Chronicling America. On the other hand, the methods in periodical studies encourage attention to the juxtaposition of visual features on a page. Starting with these two methods, we developed a set of preliminary working questions. How do the visual features on the page of a newspaper interlock and mutually inform each other? If we look at all of those features holistically, what kinds of styles and ideas can we detect over the lifespan of a single newspaper? More broadly, what added perspective can we gain by using the Newspaper Navigator to trace the sum of newspaper layouts over hundreds of publications? Finally, can we establish a “baseline” for newspaper production in the later 19th and early 20th century United States that would allow us to see the critical departures made by multi-ethnic newspapers in the service of social justice during moments of elevated racism or xenophobia?
Adapting the Newspaper Navigator dataset to answer these questions required us to collect
additional data about the subsets of multi-ethnic newspapers within Chronicling America.
This information was partially extracted from the bibliographic data of newspaper titles in the
machine-readable cataloging (MARC) format. All records for newspapers in Chronicling
America follow the cataloging standards of the Cooperative ONline SERials Program (CONSER)
[
The last step in collecting and organizing our data was combining MARC fields and structured data generated from the text of title essays to find relevant publications. Metadata from the title essays allowed us to select only ethnic newspapers with identifiable editors. From this subset, we selected MARC fields used by cataloguers to record title changes, race, ethnicity, and languages associated with each publication. We initially hoped to find an indicator of visual change within the metadata for newspapers. We considered that serials with more than one language edition, language notes, or diferent titles over their publication runs would feature changes to visual and layout elements of newspapers to mark these relationships or changes.1 Title changes, for example, are a cataloguing practice for linking preceding and succeeding titles of a publication that has had a substantial name change, which we assumed would also produce modifications to the graphic elements on the page. We were able to parse these metadata fields where the change inherent in the seriality of newspapers is recorded, which in turn provided parameters for capturing diference, similarity, and linking in an otherwise static knowledge organization system.
1The 780 and 785 fields indicate all preceding and succeeding titles for a publication, respectively. The 546 ifeld describes the language of a resource while the 765 indicates the original language of a translated publication and the 775 captures other language editions of a publication.
The first visual output we experimented with were heat maps, created with Newspaper
Navigator data that encodes the positions of visual features on a newspaper page. Inspired by
PageOneX [
This approach facilitates several comparative opportunities. We rendered multiple visual categories into heat maps to understand the interplay between visual components in a single publication. We also utilized the heat map graphics to compare diferent newspapers using the same visual categories. The heat maps ofered a useful preliminary system for studying newspaper layout as historical information.
Heat maps added greater nuance to our historical questions about newspaper layout. For example, what are the relationships between visual content, editorial practices, and print technology? What are the roles of regional and local context in predicting layout? How do we account for journalistic trends, such as the rise of syndicated content or nationally-circulating advertisements?
2The Opelousas Courier is described in Chronicling America as a Louisiana multilingual newspaper, with content in French and English. https://chroniclingamerica.loc.gov/lccn/sn83026389/marc/.
The heat maps showed the need for further exploration. In analyzing one publication at a time, the heat maps reproduced the methods of archival periodical studies, even in visually abstracted ways. While the MARC records helped us to identify the titles we were most interested in rendering into heat maps, this piecemeal approach to newspaper metadata and layouts proved only a first step.
Our next experiment attempted to scale up possible representations to refocus on illustrating
change and similarity across tens, hundreds, and even thousands of publications. Our resulting
exploratory model utilized the quantified heat maps to define a metric over visual similarity.
In particular, the distance between two newspaper titles can be captured by the residuals
from subtracting two heatmaps from one another. As an initial approach, we segmented each
newspaper title’s run into individual years. For each title and year, we then treated the seven
resulting heat maps (one for advertisements, one for headlines, etc.) collectively as a single,
high-dimensional vector. In order to interpret the clusters in this high-dimensional space, we
utilized T-SNE for dimensionality reduction [
This process resulted in exploratory constellations of newspaper titles, as shown in Figure 3. In this figure, we also show a close-up of one such “similarity cluster,” revealing that three Black newspapers (The Washington Bee, The Appeal, and The Colored American) are largely grouped together across years. In Figure 4, we show example front pages for these three titles from years included within the cluster. All six pictured front pages feature visual content on the center of the page. Notably, all six feature photographs and illustrations of individuals, a trend that is even more apparent when browsing these front pages across diferent issues. These images demonstrate that the similarity clusters, generated using Newspaper Navigator’s rubric of seven content classes, accurately reflect common visual patterns of layout in distinct newspapers across the Black newspapers held in Chronicling America. The shared qualities across each of the three newspapers pictured in Figure 4 are suggestive. They are roughly contemporaneous African American newspapers that locate specific types of images near the center of the front page. These results indicate the capacity of the method to produce a multifaceted research question: what can we learn from the use of visual culture and portraiture in the material pages of late nineteenth-century African American newspapers?
These constellations of similarity provide a means to uncover small networks to focus on and interpret at a much more granular level. This is not a fully quantitative representation, but rather an exploratory framework for identifying and interpreting networks of related publications and the editorial practices that shape them. Given our overarching interest in the historical trajectories and meanings of newspaper formats, these visualizations show great promise. The constellation map is a model for how to convert raw bounding box information into interpretable provocations about historical similarity. Similarity clusters introduce visual layouts as clues to a shared context, inviting new research into the diferent historical traditions, technological moments, or political causes that comprise the histories of editorship.
Our ongoing collaboration ofers three central findings to date. First, this work finds that full level CONSER MARC records provide useful faceted metadata for accurately selecting multi-ethnic newspapers in machine-learning applications. Specifically, the most value comes from fields that register change over time, such as preceding and succeeding titles, and describe relationships between diferent editions of the same publication. Further metadata enhancement focused on identifying ethnic editors will significantly improve the interpretative value of visualizations and pattern recognition from large-scale collections like Chronicling America.
Second, this collaboration reveals how computational notions of layout similarity can be leveraged for humanistic inquiry. Using the machine learning-constructed Newspaper Navigator dataset of visual content, we have generated heat maps and constellation maps for exploring trends of visual layout within and across newspaper titles, respectively. These visualizations provide us with new visual grammars and afordances for excavating editorial practices.
Third, this work required us to find a collaborative working process for creating multidisciplinary methods of analysis that engage with machine learning, library science, and the humanities. Rather than trying to blend or transplant our respective research methods, we focused on creating a shared vocabulary that could speak to current research agendas in each of the relevant fields. This set of theoretical exchanges required iterative conversations, frequently pedagogical in nature, to find a method that is not merely extractive and static but generative and dynamic. This process will help guide the future growth of conversations in computational periodicals research.
Our roadmap for future work is guided by the provocations ofered throughout this paper. First, we plan to further utilize MARC data and the constellation maps in concert with one another in order to inform our understanding of both. The MARC data has the capacity to guide our navigation and understanding of the constellation maps; conversely, the constellation maps provide mechanisms for record enhancement by allowing scholars, catalogers, and librarians alike to sift through incomplete legacy records of ethnic publications. We ofer this direction of future work in pursuit of uncovering editorship in ethnic presses. We plan to refine our similarity metrics and visualizations from a computational perspective in pursuit of this goal as well. The results of this research will drive new inquiry into the longer and varied histories of radical dissent and veiled protest in the multi-ethnic press.
The authors would like to thank and acknowledge Dr. Molly Hardy, Senior Program Officer at the National Endowment for the Humanities, along with Georgia Higley and Vanessa Mitchell from the Serial and Government Publications Division at the Library of Congress, for sharing their expertise. This work would not be possible without Dr. Hardy’s input and advice.