Word Category Arcs in Literature Across Languages and Genres (2023)

Abstract: Word category arcs measure the progression of word usage across a story. Previous work on arcs has explored structural and psycholinguistic arcs through the course of narratives, but so far it has been limited to \textit{English} narratives and a narrow set of word categories covering binary emotions and cognitive processes. In this paper, we expand over previous work by (1) introducing a novel, general approach to quantitatively analyze word usage arcs for any word category through a combination of clustering and filtering; and (2) exploring narrative arcs in literature in eight different languages across multiple genres. Through multiple experiments and analyses, we quantify the nature of narratives across languages, corroborating existing work on monolingual narrative arcs as well as drawing new insights about the interpretation of arcs through correlation analyses.

## introduction throughout history, the narrative has been an essential medium for communicating and transferring information. the study of the structure of narratives has roots in the ancient greek philosophers but did not gain much interest until the last few hundred years. one of the most well-known structures is freytag's pyramid, the dramatic arc of german novelist and playwright gustav @xcite , which contains five stages: exposition, rising action, climax, falling action, and resolution. many others have hypothesized sets of universal structures into which all narratives can be classified. for example, @xcite argued that a story has three basic plots that end with a happy, unhappy, or tragic ending. @xcite proposed seven basic plots: overcoming the monster, rags to riches, the quest, voyage and return, comedy, tragedy, and rebirth. others have posited 20 plots @xcite and even 36 plots @xcite that are universal across great stories. regardless of the actual number of different plots, one point is clear: the structures of plots naturally vary. a story's structure gives coherence to the entire plot and can be mathematically represented as a function over time, or a narrative arc. the american writer kurt vonnegut claimed in his famously rejected master's thesis @xcite that every story can be plotted as such a curve, where the xaxis is the duration of the story, and the y-axis is a character's "ill fortune -great fortune" @xcite . this was a revolutionary notion at the time and only recently has been computationally investigated. following existing computational work @xcite @xcite , we consider a narrative arc as a measure of word usage (count) across a story. we use the term word category arc to emphasize that this arc is measured by examining words that belong to certain categories. this count may be z-score standardized to better understand the relative usage of certain words across a story. thus, an arc provides a high-level structural overview of a narrative. all cultures tell stories, but the manner in which the stories are told differs. narrative arcs are one method for quantifying the cultural differences in stories. we first describe a general framework for analyzing arcs in a narrative that follows closely from vonnegut's claim. to compute arcs, we measure the usage of words in a given word category, such as positive emotion words in liwc @xcite , a popular dictionary of english words associated with various psychometric properties. however, liwc is not available for many of the world's languages. thus, we develop an automatic method to translate the english liwc into other languages. our automatic translations exhibit high overlap with an existing manual chinese translation @xcite , indicating that machine translation is a viable alternative to human translations, which are often tedious and costly. using our translated liwc dictionaries, we perform in-depth analyses of many categories of arcs, including those that represent structure and emotion, in eight different languages. next, we investigate narrative arcs across stories in multiple languages from project gutenberg, a large repository of public-domain books. while different languages largely exhibit similar arcs on average, we find that different genres of stories follow diverse narrative arcs, which we concretely quantify through correlation analyses. finally, we demonstrate how to interpret clusters of arcs, and how similar word categories can be identified by their arcs even when the categories have no words in common. code to reproduce our experiments is available at github.com/wswu/arcs. ## data and dictionaries our analysis requires two main resources: a collection of narratives in multiple languages, and dictionaries with relevant word categories for the same set of languages. ## quantifying narrative arcs with our translated word dictionaries, we now investigate narrative arcs across languages. ## conclusion narrative arcs, operationalized as word category arcs, model word usage across the timeline of a narrative. they are powerful tools that allow us to not only gain a high-level overview of a narrative's structure but also enable us to identify similarities across languages and genres. in order to quantify narrative arcs across languages, we present a method for automatically translating wordlists such as liwc, which we validate with an existing chinese translation of liwc. we then apply our translated dictionaries in eight languages to analyze narrative arcs in project gutenberg fiction books. we first investigate clustering to interpret narrative structure according to kurt vonnegut's claims. next, we investigate story structure, showing that boyd et al. (2020)'s created word categories findings largely hold across languages. we then perform correlation studies, interpreting narrative arcs with respect to word categories, genres, and languages. analyzing categories, we discover and explain positive correlations between several categories, even when they have no words in common. analyzing genres, science fiction and short stories have a higher usage of sexual words at the beginning of the story in order to hook the reader. analyzing languages, we find that a high correlation between certain categories like death and informal words can indicate a typological relation. this work investigates how narrative arcs differ across various dimensions; we leave the question of why to future work.