Actual Fictions presents a data-driven history of the 20th and 21st century Dutch language novel. Using data science methods, this project analyses a series of 5 large scale corpora ranging from the 1900s up until the 2010s in light of emerging societal trends. More specifically, it focuses on the extent to which the Dutch novel has transformed in light of a range of emancipatory movements. Techniques such as text mining, machine learning and social network analysis are employed to test the hypothesis that improvements in the social position of e.g. women, migrants, the working class, the young are reflected in the fictional worlds depicted in novels.
Theoretically, this project is grounded in age-old discussions about ‘mimesis’, fiction’s function in society and the literary medium of the novel. By employing a wider definition of ‘form’ (as proposed by Caroline Levine), it aims to bridge the deadlock that seems to have been reached between historicist and formalist approaches to literature. Methodologically, it translates insights from data science, cultural analytics and the digital humanities to the practice of critical close reading and cultural analysis.