Representing and Modeling Cultural Relevance in Corpora for Historical Analysis

Working with large corpora is one of the strengths of computational literary studies (CLS). Collecting metadata is the essential step for interpreting analytical results. So far, large corpora focus on non-historical metadata such as gender or general genres. Historical metadata, if any, are collected as epoch or periods of publication, and with a growing interest also in the historicity of genres. In CLS, there is, however, a growing interest in more specific socio-historical changes. Pursuing this interest necessitates collecting contextual metadata. One of the most important dimensions of a social history of literature is that of cultural relevance. So far, corpora tend to have several methodological weaknesses: Firstly, as most studies do not pay attention to the aspect of relevance, most corpora give all texts the same weight. In studies that have an interest in prestige, it is common practice to model these categories in a non-historical fashion or from the perspective of present readers. Secondly, most corpora have a bias towards highly canonical texts because canonical texts are already digitized whereas the ›big unread‹ awaits to be exploited. To investigate the historical change of cultural relevance, both weaknesses have to be overcome.

The central assumption behind the poster that shall be presented is that the forms of cultural relevance are much more diverse and that significant new historical insights can be gained by representing this diversity depending on specific research interests. Hence, the poster will have three areas that correspond to methodological steps, respectively: 1) It shows in a spreadsheet the current state of research. 2) It discusses the advantages and shortcomings of possible models of prestige based on visualizations starting from a specific interest in the dependency between media formats and genre semantics. 3) Using different forms of visualization, the poster demonstrates the impact of cultural relevance beyond prestige based on a genuine new corpus.