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Historical newspaper collections provide a wealth of information about the past. Although the digitization of these collections significantly improves their accessibility, a large portion of digitized historical newspaper collections, such as those of KBR, the Royal Library of Belgium, are not yet searchable at article-level. However, recent developments in AI-based research methods, such as document layout analysis, have the potential for further enriching the metadata to improve the searchability of these historical newspaper collections. This paper aims to discuss the aforementioned issue.

In this paper, the authors explore how existing computer vision and machine learning approaches can be used to improve access to digitized historical newspapers. To do this, the authors propose a workflow, using computer vision and machine learning approaches to (1) provide article-level access to digitized historical newspaper collections using document layout analysis, (2) extract specific types of articles (e.g. feuilletons – literary supplements from Le Peuple from 1938), (3) conduct image similarity analysis using (un)supervised classification methods and (4) perform named entity recognition (NER) to link the extracted information to open data.

The results show that the proposed workflow improves the accessibility and searchability of digitized historical newspapers, and also contributes to the building of corpora for digital humanities research. The AI-based methods enable automatic extraction of feuilletons, clustering of similar images and dynamic linking of related articles.

The proposed workflow enables automatic extraction of articles, including detection of a specific type of article, such as a feuilleton or literary supplement. This is particularly valuable for humanities researchers as it improves the searchability of these collections and enables corpora to be built around specific themes. Article-level access to, and improved searchability of, KBR's digitized newspapers are demonstrated through the online tool (https://tw06v072.ugent.be/kbr/).

This content analysis examines the historical representation of Margaret Sanger within trade books. From the framework of the historiography, this paper unpacks how common curricular resources depict an American icon with a complicated past.

In this paper, the author conducted a content analysis of biographies and expository compilations featuring Sanger. The entire data pool were sampled and analyzed.

The trade books, particularly the biographies, historically represented Sanger in most categories. Sanger's international direct action and eugenics were two misrepresented areas. Expository compilations, with more limited space than biographies, contained more omissions and minimized or vague depictions of key areas. Findings did not appear dependent upon date of publication.

This study explores an icon of America's free speech battles and birth control rights at a time when culture wars are shaping current events. No researchers have previously explored Sanger's historical representation within trade books.

This study aims to analyze corporate mail and wire fraud penalties, using bounded rationality in decision-making and assessing internal and external influences on prosecutorial choices.

The study analyzed 467 cases from 1992 to 2019, using data from the Corporate Prosecution Registry of the University of Virginia School of Law and Duke University School of Law. It examined corporations facing mail and wire fraud charges and other fraud crimes. Multiple regression linked predictor variables to the dependent variable, total payment.

The study found that corporate penalties tend to be lower for financial institutions or corporations in countries with US free trade agreements. Conversely, penalties are higher when the company is a U.S. public company or filed in districts with more pending criminal cases.

This study’s originality lies in applying the bounded rationality model to assess corporate prosecutorial decisions, unveiling external factors’ influence on corporate penalties.