Tracking the Consumption Junction: Temporal Dependencies between Articles and Advertisements in Dutch Newspapers

Historians have regularly debated whether advertisements can be used as a viable source to study the past. Their main concern centered on the question of agency. Were advertisements a reflection of historical events and societal debates, or were ad makers instrumental in shaping society and the ways people interacted with consumer goods? Using techniques from econometrics (Granger causality test) and complexity science (Adaptive Fractal Analysis), this paper analyzes to what extent advertisements shaped or reflected society. We found evidence that indicate a fundamental difference between the dynamic behavior of word use in articles and advertisements published in a century of Dutch newspapers. Articles exhibit persistent trends that are likely to be reflective of communicative memory. Contrary to this, advertisements have a more irregular behavior characterized by short bursts and fast decay, which, in part, mirrors the dynamic through which advertisers introduced terms into public discourse. On the issue of whether advertisements shaped or reflected society, we found particular product types that seemed to be collectively driven by a causality going from advertisements to articles. Generally, we found support for a complex interaction pattern dubbed the consumption junction. Finally, we discovered noteworthy patterns in terms of causality and long-range dependencies for specific product groups. All in, this study shows how methods from econometrics and complexity science can be applied to humanities data to improve our understanding of complex cultural-historical phenomena such as the role of advertising in society

Blind Dates: Examining the Expression of Temporality in Historical Photographs

This paper explores the capacity of computer vision models to discern temporal information in visual content, focusing specifically on historical photographs. We investigate the dating of images using OpenCLIP, an open-source implementation of CLIP, a multi-modal language and vision model. Our experiment consists of three steps: zero-shot classification, fine-tuning, and analysis of visual content. We use the \textit{De Boer Scene Detection} dataset, containing 39,866 gray-scale historical press photographs from 1950 to 1999. The results show that zero-shot classification is relatively ineffective for image dating, with a bias towards predicting dates in the past. Fine-tuning OpenCLIP with a logistic classifier improves performance and eliminates the bias. Additionally, our analysis reveals that images featuring buses, cars, cats, dogs, and people are more accurately dated, suggesting the presence of temporal markers. The study highlights the potential of machine learning models like OpenCLIP in dating images and emphasizes the importance of fine-tuning for accurate temporal analysis. Future research should explore the application of these findings to color photographs and diverse datasets

The Leonardo Code: Deciphering 50 Years of Artistic/Scientific Collaboration in the Texts and Images of Leonardo Journal, 1968-2018

International audienceLeonardo (1968-present), published by MIT Press, is the leading international peer-reviewed publication on the relationship between art, science and technology, making it an ideal dataset to analyze the emergence of such complex collaborations over time. To identify and analyze both the visible and latent interaction patterns, the research employs different granularities of data (article texts, images, publication dates, authors, their places of affiliation and disciplines) as part of a multimodal approach. Using a convolutional neural network, we examined the features of the images to analyze the modes of representing (and actually doing) art, science or engineering. We paired these features with information extracted using text mining to examine the relationships between the visual and the textual over time

Classifying Latin Inscriptions of the Roman Empire: A Machine-Learning Approach

Large-scale synthetic research in ancient history is often hindered by the incompatibility of tax- onomies used by different digital datasets. Using the example of enriching the Latin Inscriptions from the Roman Empire dataset (LIRE), we demonstrate that machine-learning classification mod- els can bridge the gap between two distinct classification systems and make comparative study possible. We report on training, testing and application of a machine learning classification model using inscription categories from the Epigraphic Database Heidelberg (EDH) to label inscriptions from the Epigraphic Database Claus-Slaby (EDCS). The model is trained on a labeled set of records included in both sources (N=46,171). Several different classification algorithms and parametriza- tions are explored. The final model is based on Extremely Randomized Trees algorithm (ET) and employs 10,055 features, based on several attributes. The final model classifies two thirds of a test dataset with 98% accuracy and 85% of it with 95% accuracy. After model selection and evaluation, we apply the model on inscriptions covered exclusively by EDCS (N=83,482) in an attempt to adopt one consistent system of classification for all records within the LIRE dataset

The visual digital turn: Using neural networks to study historical images

Digital humanities research has focused primarily on the analysis of texts. This emphasis stems from the availability of technology to study digitized text. Optical character recognition allows researchers to use keywords to search and analyze digitized texts. However, archives of digitized sources also contain large numbers of images. This article shows how convolutional neural networks (CNNs) can be used to categorize and analyze digitized historical visual sources. We present three different approaches to using CNNs for gaining a deeper understanding of visual trends in an archive of digitized Dutch newspapers. These include detecting medium-specific features (separating photographs from illustrations), querying images based on abstract visual aspects (clustering visually similar advertisements), and training a neural network based on visual categories developed by domain experts. We argue that CNNs allow researchers to explore the visual side of the digital turn. They allow archivists and researchers to classify and spot trends in large collections of digitized visual sources in radically new ways

Advertising Gender - Using Computer Vision to Trace Gender Displays in Historical Advertisements, 1920-1990

This study applies computer vision techniques to examine the representation of gender in historical advertisements. Using information on the relative size, position, and gaze of men and women in thousands of images, we chart gender displays in Dutch newspaper adverts between 1920 and 1990