Finding genre signals in academic writing

Abstract

This article proposes novel methods for computational rhetorical analysis to analyze the use of citations in a corpus of academic texts. Guided by rhetorical genre theory, our analysis converts texts to graph-theoretic graphs in an attempt to isolate and amplify the predicted patterns of recurring moves that are associated with stable genres of academic writing. We find that our computational method shows promise for reliably detecting and classifying citation moves similar to the results achieved by qualitative researchers coding by hand as done by Karatsolis (this issue). Further, using pairwise comparisons between advisor and advisee texts, valuable applications emerge for automated computational analysis as formative feedback in a mentoring situation.