Mining Historical Advertisements in Digitised Newspapers

Historians have turned their focus to newspaper articles as a proxy of public discourse, while advertisements remain an understudied source of digitized information. This paper shows how historians can use computational methods to work with extensive collections of advertisements. Firstly, this chapter analyzes metadata to better understand the different types of advertisements, which come in a wide range of shapes and sizes. Information on the size and position of advertisements can be used to construct particular subsets of advertisements. Secondly, this chapter describes how textual information can be extracted from historical advertisements, which can subsequently be used for a historical analysis of trends and particularities. For this purpose, we present a case study based on cigarette advertisements

Event detection in field sports video using audio-visual features and a support vector machine

In this paper, we propose a novel audio-visual feature-based framework for event detection in broadcast video of multiple different field sports. Features indicating significant events are selected and robust detectors built. These features are rooted in characteristics common to all genres of field sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested generically across multiple genres of field sports including soccer, rugby, hockey, and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable

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

Automated Parsing of Personal Identity Facets for a Collection of Visual Images

Collections of digitized, historical images serve as rich primary sources for digital humanities research, though access to these resources has been hindered by inadequate subject metadata. In this study, researchers explored the feasibility of performing subject analysis for a collection of historical images of persons through an automated procedure. Building on previous work that developed a faceted system for representing the identities of persons depicted in 19th century visual images, the present work attempted to automate the process of person and facet parsing for images from the A.S. Williams III Collection at the University of Alabama. A case-based model was built and used to analyze image titles. Compared to a manual control process, the automated model achieved a 95% success rate in parsing persons and an 85% success rate in parsing facets. Errors in parsing were more likely to occur for images of multiple persons, as well as those labeled with incomplete or uncertain names. Findings offer further support for faceted analysis of personal identity in historical materials, and reveal the potentials of automated, text-based methods of enhancing subject access for large visual image collections

The Role of Context and Content on Recognition Accuracy in Virtual Worlds

We investigate differences in recognition accuracy of visual vs. text content presented in two contexts – virtual world (imagery processing) vs. Web browser (discursive processing). In three studies, one completed and two planned, we address the conditions under which a match or mismatch between content and context improves recognition accuracy

AD or Non-AD: A Deep Learning Approach to Detect Advertisements from Magazines

The processing and analyzing of multimedia data has become a popular research topic due to the evolution of deep learning. Deep learning has played an important role in addressing many challenging problems, such as computer vision, image recognition, and image detection, which can be useful in many real-world applications. In this study, we analyzed visual features of images to detect advertising images from scanned images of various magazines. The aim is to identify key features of advertising images and to apply them to real-world application. The proposed work will eventually help improve marketing strategies, which requires the classification of advertising images from magazines. We employed convolutional neural networks to classify scanned images as either advertisements or non-advertisements (i.e., articles). The results show that the proposed approach outperforms other classifiers and the related work in terms of accuracy.http://dx.doi.org/10.3390/e2012098

A Guide to the Duane D. Pearsall Papers

A finding aid for the Duane Pearsall Papers. The digital collection covers the time period from his birth until his death, 1922-2010. Duane Pearsall is best known for developing the battery operated smoke detector for residential use, an accidental invention saving lives since the 1970s. He was instrumental in changing commercial and residential building codes to require smoke detection devices. Since the mid-1970s, thousands of lives saved can be attributed to the most significant lifesaving innovation of the 20th century, the battery powered smoke detector. This collection contains records he kept about his life, his work in fire protection and his work for small business. He was an enlightened capitalist, an early adopter of profit-sharing, hiring women and minorities, and teaching his employees their place in the free-enterprise system known as capitalism