Methodology

This project aims to analyze the representation of class and poverty in U.S. and U.K. fiction and life writing by comparing works written by both working-class and middle-class authors. The research builds on an on-going book project by Lennard Davis that describes different patterns of representation based on the economic background of novelists. Thus, narratives that were written by middle-class or upper-class authors frequently show a focus on violence, drug use, urban decay, or other living conditions that is, in comparison, largely absent from proletarian writing.

In a first step, a database will compile metadata based on information from about fifty texts by working class (or “endo”) authors and the same number of middle- or upper-class (or “exo”) writers that describe poverty and working-class lives. This database will be used to construct a digital corpus in a second step. While we envisage that many of these texts will be accessible in digital form, it might become necessary to digitize some of the lesser known works.

Building on working hypotheses developed by Davis, we will then seek to establish whether there are quantitative differences between “endo” and “exo” representations of class. Several established methodologies within digital literary studies may be drawn on for this project, including natural language processing (NLP), social network analysis, topic modeling, and sentiment analysis. NLP methods will show whether these groups of texts differ on a basic word level, including the use of key words, collocations, or most frequent words. NLP may also serve as an indicator for the presence of non-standard sociolects used by working-class characters and the use of certain adjectives or verbs associated with them. These findings will shed light on the description of living conditions and character agency within these works. Social network analysis has the potential to reveal differing representations of the social milieus of working-class characters, their integration into family units or participation in larger networks (i.e. labor collectives or ethnic neighborhoods). At a more complex level, word embeddings (Word2Vec) may highlight associative clusters that establish the wider context of key words. Similarly, so-called topic models allow for the identification of narrative themes running through corpora of texts. Finally, while sentiment analysis currently works best for shorter, non-literary texts, for which it was developed, it might be worth exploring whether working and middle-class characters in the corpus are associated with positive or negative emotions.

Authors and Collaborators: Lennard Davis (UIC), Alexander Dunst (University of Paderborn, Germany), Hannah Huber (Sewanee: The University of the South), Carla Barger (UIC), Travis Mandell (UIC), Stephan Drechsler (University of Paderborn, Germany), Katie Brandt (UIC), Justin Allen (UIC)

Abstract by Alexander Dunst.

The Endo/Exo Writers project is a collaboration between the University of Illinois at Chicago, Paderborn University, and The Center for Southern Studies at Sewanee. The project has been made possible by the continuing efforts of graduate students at these institutions. You can find more information on the collaborators on the Our Scholars page.