'We can't read it all': Theorizing a hermeneutics for large-scale data in the humanities with a case study in stylometry

Computational methods often produce large amounts of data about texts, which create theoretical and practical challenges for textual interpretation. How can we make claims about texts, when we cannot read every text or analyze every piece of data produced? This article draws on rhetorical and literary theories of textual interpretation to develop a hermeneutical theory for gaining insight about texts with large amounts of computational data. It proposes that computational data about texts can be thought of as analytical lenses that make certain textual features salient. Analysts can read texts with these lenses, and argue for interpretations by arguing for how the analyses of many pieces of data support a particular understanding of text(s). By focusing on validating an understanding of the corpus rather than explaining every piece of data, we allow space for close reading by the human reader, focus our contributions on the humanistic insight we can gain from our corpora, and make it possible to glean insight in a way that is feasible for the limited human reader while still having strategies to argue for (or against) certain interpretations. This theory is demonstrated with an analysis of academic writing using stylometry methods, by offering a view of knowledge-making processes in the disciplines through a close analysis of function words.