Speaker
Description
The Wiener Salonblatt was one of the most popular society magazines of fin-de-siècle Vienna. The highly illustrated weekly was published from 1870 to 1938 and primarily featured short notices about personal achievements, travels, and family matters – mostly contributed by members of the nobility. These messages fulfilled a function similar to that of posts on today’s social media platforms: maintaining social connections and cultivating a public persona.
The content of the Wiener Salonblatt is currently being digitized using AI-assisted methods as part of a dissertation project. The overarching research goal is to better understand the self-representation and transformation of the late Habsburg nobility by identifying topical trends, geospatial patterns, and historical networks. A central component of this process is the extraction of all images, resulting in a corpus of approximately 30,000 photographs and xylographs published between 1870 and 1938 – consisting mainly of portraits of members of the Habsburg nobility.
This period is crucial in three respects: first, for the development of portrait photography, encompassing technological innovations such as the focal-plane shutter (1883), roll film (1888), and the emergence of the 35mm camera (1920s). Second, printing techniques like halftone and intaglio printing, both developed in the late 19th century, greatly contributed to the rise of illustrated magazines. Third, the Habsburg nobility experienced a series of transformative events, including the electoral reform of 1907 and the law abolishing nobility in 1919.
With these three perspectives in mind, the resulting image corpus offers a unique opportunity to analyse the visual self-representation of the Habsburg nobility, to identify photographic trends, and to trace the evolution of portrait photography. Research interests include the appearance of smiles in portraits, the changing composition of aristocratic family photographs, and the depiction of pets.
To process this extensive visual material, various AI-assisted methods are employed. These include layout recognition with Transkribus, image analysis using CLIP, and facial recognition with DeepFace. The poster will present the methodological workflow of this ongoing project and showcase preliminary results.