Predicting author gender using machine learning algorithms: Looking beyond the binary

Abstract

This paper explores the relationship between digital humanities studies that utilize computer algorithms to identify author gender and feminist and queer literary theory. I argue that utilizing computer algorithms to sort literature into the categories "authored by a male"or "authored by a female"is too reductive in its treatment of gender as binary. However, I suggest computer algorithms could be utilized to explore the performative aspects of author gender and to ask larger questions about algorithmic criticism, the author as a subject, and the relationship between morphological and cultural properties of texts.