‘Tecnologica cosa’: Modeling Storyteller Personalities in Boccaccio’s ‘Decameron’ (2021)

Abstract: We explore Boccaccio’s Decameron to see how digital humanities tools can be used for tasks that have limited data in a language no longer in contemporary use: medieval Italian. We focus our analysis on the question: Do the different storytellers in the text exhibit distinct personalities? To answer this question, we curate and release a dataset based on the authoritative edition of the text. We use supervised classification methods to predict storytellers based on the stories they tell, confirming the difficulty of the task, and demonstrate that topic modeling can extract thematic storyteller “profiles.”

## introduction the decameron is a masterpiece of medieval italian literature. completed by 1353, the text is often referred to as "l'umana commedia" (the human comedy), foot_0 a name meant to strike a contrast in subject matter (and a parity in importance) with dante's well-known "divina commedia" (divine comedy) @xcite . in a structure similar to geoffrey chaucer's the canterbury tales, it is a collection of 100 stories (novelle) woven together in the context of a frame tale: an honorable brigade (brigata) composed of 7 women and 3 men who have fled the ravages of plague in florence to the relative seclusion of the tuscan countryside. 2 the 100 novelle are told by the 10 brigata members over 10 days, with each day assigned a theme spanning matters of love, wit, and trickery. while scholarship for the canterbury tales has engaged with both the stories and the storytellers @xcite @xcite , storyteller identity has received relatively less attention in the decameron. instead, literary research has tended to address themes and stories @xcite @xcite . treatment of storyteller identity has thus far been sparse, perhaps due to storyteller personalities seeming generally foot_2 difficult to distinguish at a high level via close reading. 4 we therefore ask: do the members of the brigata exhibit distinct storytelling personalities? we emphasize that this is not a question of authorship, as the text is attributed to boccaccio alone, but rather one of thematic and stylistic differences among the fictional characters he depicts. to approach this question, we use computational tools to elicit patterns from the text-patterns that may have thus far remained elusive to scholars and could help constitute unique storyteller identities. this case study highlights several challenges for digital humanities research. while the decameron is a popular and well-studied text, it is written in medieval italian, for which there are few language modeling resources; this forces us to rely on language-agnostic methods like classification and topic modeling (section 4). moreover, while some digitized resources do exist (brown university italian studies department; @xcite , the text required multiple rounds of curation to be used for a computational study. to facilitate future digital decameron scholarship, we release our user-friendly digital version (section 3). 5 in order to build a training corpus for this domain, similar curation will be necessary for other digitized medieval italian texts, including additional works by boccaccio and authors such as dante and petrarch. taken together, our classification and topic modeling results support existing humanist scholarship concerning storyteller identity and suggest new questions for further inquiry. more broadly, our work here serves as preliminary evidence that such tools can be useful for highly specialized academic digital humanities work-in non-english and nonstandard (e.g., bygone language variant) domains. ## related work as one progresses through the 10 days of the decameron, different members of the brigata seem to develop distinct storytelling personalities. dioneo frequently tells bawdy tales, pushing the bounds of decorum. emilia seems like she does not quite fit in with the rest of the group, and in fact may be (though we cannot be certain) an actual political outsider-the sole ghibelline in the group of guelphs @xcite . lauretta can perhaps be cast as a "bearer of bad news," according to @xcite . notwithstanding such standalone examples, scholarship has not addressed whether each of the 10 brigata members have clearly identifiable storytelling personalities. as there is no literary consensus, we apply computational tools to extract patterns that might be difficult for human readers to elicit. prior work in digital humanities has studied a variety of narrative questions across corpora of multiple texts. for example, research in cultural analytics has compared narrative structure @xcite @xcite , character arcs and relationships @xcite , and authorship attribution @xcite . authorship can also be modeled as a latent factor in topic models @xcite . we do not explicitly model authors (or in our case, narrators) but instead rely on a simpler model to extract cross-cutting, interpretative themes. moreover, unlike studies of focalization @xcite , we make no attempt to model the perspective or views of a character, but rather simply ask if characters are in any way distinguishable. in computational studies that similarly focus on sections of a single work, @xcite compare sections of italo calvino's invisible cities and @xcite investigate distinguish-ing narrative voices in t.s. eliot's the waste land. @xcite circumvent data size limitations by relying on large, pretrained contextual models to cluster the cities and compare thematic patterns; @xcite rely on preexisting tools that elicit english-language features, including parts of speech and verb tense. such pretrained models and featurization tools, while available for modern italian @xcite , are unavailable for the quite different medieval italian of the decameron @xcite . we instead use language-agnostic computational tools for our experiments, which come with the added benefit of interpretability (section 4). ## curating a decameron dataset we constructed a json dataset of the decameron from an xml version hosted online by the sapienza university of rome @xcite . this digitized version is based on vittore branca's authoritative text @xcite , and was published online in 2003 in the (at the time standard) tei p4 format @xcite . tei p4 contains a variety of metadata that interrupt contiguous portions of boccaccio's text, which does not make the format amenable to commonlyused tools. we therefore spent considerable time simplifying this format to be more easily manipulable for modern data analysis. we manually and repeatedly verified that our curation process retained the integrity of the text. 6 where appropriate, we added metadata to annotate novelle, such as the novella storyteller, which was absent in the existing online version. unlike the tei p4 format, we avoid placing these metadata within the text of individual novelle, and provide scripts for those that wish to remove these metadata for their analyses. we release this dataset publicly. our hope is that our online version of branca's text will be more accessible to scholars of medieval italian interested in engaging with digital tools, as the simplicity of our format should lower the barrier to entry for both computational and humanist scholars interested in the decameron. ## case study: constructing storyteller profiles in boccaccio's decameron we use the problem of decameron storyteller identity as a case study for exploring the challenges and opportunities for using digital humanities techniques in specialized literary domains. in particular, we investigate how such tools can be useful for 1) a small corpus containing a single text 2) modeling language that is no longer in contemporary use. while the question we ask is specific to our chosen domain-the decameron and medieval italian-we believe that the lessons we can derive are applicable to other scholarly digital humanities tasks with these same defining elements. ## conclusion and future work while our work has focused on a specific questionwhether the members of the decameron's brigata exhibit distinct storytelling personalities-we have illustrated broader lessons for small-text, specialized-language digital humanities scholarship. a central tension for work in low-resource domains is whether to focus on building tools and resources to mimic large, english-language resources or to instead work around the lack of resources by relying on methods that do not require much training data. while we have taken the latter path in this paper, we see ample opportunities for both developing models and annotating larger datasets for this domain @xcite . for example, while medieval italian is syntactically quite different from modern italian, some linguistic studies on specific texts indicate significant lexical overlap. 12 based on this observation, future work could modify existing italian contextual models for high-fidelity use on medieval italian works. for scholars of the decameron, our highlighted results indicate areas for further inquiry. for example, a close-reading analysis of the novelle could explain when and why the women storytellers talk about more "male" topics (e.g., mercantile themes) and would complement our topic modeling results. more broadly, our release of a simplified format of the digitized text will facilitate future digital humanities research related to boccaccio's decameron.