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The representation of mobility in literary narratives has important implications for the cultural understanding of human movement and migration. In this paper, we introduce novel methods for measuring the physical mobility of literary characters through narrative space and time. We capture mobility through geographically defined space, as well as through generic locations such as homes, driveways, and forests. Using a dataset of over 13,000 books published in English since 1789, we observe significant "small world" effects in fictional narratives. Specifically, we find that fictional characters cover far less distance than their non-fictional counterparts; the pathways covered by fictional characters are highly formulaic and limited from a global perspective; and fiction exhibits a distinctive semantic investment in domestic and private places. Surprisingly, we do not find that characters' ascribed gender has a statistically significant effect on distance traveled, but it does influence the semantics of domesticity. 

Over the past decade, the field of natural language processing has developed a wide array of computational methods for reasoning about narrative, including summarization, commonsense inference, and event detection. While this work has brought an important empirical lens for examining narrative, it is by and large divorced from the large body of theoretical work on narrative within the humanities, social and cognitive sciences. In this position paper, we introduce the dominant theoretical frameworks to the NLP community, situate current research in NLP within distinct narratological traditions, and argue that linking computational work in NLP to theory opens up a range of new empirical questions that would both help advance our understanding of narrative and open up new practical applications.