Digital Philology 8.2 (2019): 155-191. (Pre-publication version.) Using N-gram Analysis to Map Intertextual Networks in Old English Verse 1 Paul Battles, Hanover College Abstract: Old English poems are difficult to date, largely anonymous, and written in a formulaic idiom, which makes it difficult to establish the authorship and intertextual relationships among the various poems. This essay employs computational stylometry—specifically, n-gram analysis—and network analysis to address this problem. It proposes methods for screening out chance n-grams and for employing n-grams of various lengths within a single analysis. This analysis shows that in Old English poetry formulaic diction exists at the level of idiolect, sociolect, and language. The signed poems of Cynewulf evince idiolectal characteristics; along with Andreas, Christ III, The Phoenix, and Guthlac A and B, they also form a poetic sub-tradition (sociolect). The list of things we do not know about Old English poetry is long. We do not know where, when, or by whom the vast majority of poems were written—or even if “written” is the correct word to describe how they were composed. We do not know by whom the surviving poems were read, nor how widely they circulated. We do not know whether some poems allude to others. And, given the formulaic nature of the poetic language, we are still uncertain about the extent to which Anglo-Saxon vernacular poets can be said to have individual styles. Even when we can ascertain some facts concerning a poem’s composition, the set of things known with absolute certainty is small. The works of Cynewulf—who 1 This essay is dedicated to Charles D. Wright, Emeritus Professor of Medieval Studies and English at the University of Illinois at Urbana-Champaign, in thanks for his unflagging support and mentorship throughout the years. I gratefully acknowledge the feedback provided at various stages of this project by Charles D. Wright, T. A. Shippey, Dominique Battles, Bill Altermatt, Theresa Wilson, and Digital Philology’s anonymous reviewers. I am especially thankful to Haris Skiadas for his help with various statistical concepts and also for suggesting that I look into network graphs for visualizing intertextual relationships. Of course, any errors that remain are my own.