Patrik Lundell: "1800-talets Infobahn: Offentligheten som tidningsinfrastruktur och delat innehåll"

Patrik Lundell, Örebro universitet

På seminariet presenterar jag det helt nyligen påbörjade VR-projekt som jag genomför tillsammans med Johan Jarlbrink (UMU) och Charlotte Nilsson (LU). Det är en spin-off på ett annat, ännu pågående, projekt (https://blogit.utu.fi/informationsfloden/), vilket skapat det gränssnitt som utgör den empiriska grunden för föreliggande projekt och som hittas här: https://textreuse.sls.fi Kolla gärna – och använd det.

Här nedan följer ansökans abstract:

The dominating information technology for public discourse in the 19th century was the newspaper. And a fundamental, but neglected, feature of the time period’s press is its shared content. With digital methods and close readings, these information flows in time and space can be mapped and analysed. 

The purpose of the project is to provide an overall new interpretation of the birth of the now supposedly dying public sphere. This means challenging, and nuancing and pinning down the specifics, of a long-standing, but empirically weakly substantiated, grand narrative. With access to the whole Swedish press (some 4.5 million pages) and a powerful algorithm, the aims of the projectare 1) the mapping of the newspaper infrastructure and its information flows, 2) the description and interpretation of the directions, speed, and content of these textual flows, and 3) the understanding of the interrelations of infrastructure and content as well as of how and why patterns changed, and when and, if so, more precisely in what ways, it is justifiable to speak of a public sphere. 

While the aims of the project are directed towards the infrastructure, it also provides a solid ground for future investigations into the relations between infrastructural and societal change overall. Ultimately, the project fosters new historical knowledge on the 19th century media and communication landscape and develops ways to present and make future use of these results and the huge data set they rely on.