The Style of a Successful Story: a Computational Study on the Fanfiction Genre
Authors: Andrea Mattei, Dominique Brunato, Felice Dell'Orletta (Università di Pisa, CNR-ILC, 2020)
Topic: Textual Genres & Literature Linguistics

Abstract
This paper presents a new corpus for the Italian language representative of the fanfiction genre. It comprises about 55k usergenerated stories inspired to the original fantasy saga “Harry Potter” and published on a popular website. The corpus is large enough to support data-driven investigations in many directions, from more traditional studies on language variation aimed at characterizing this genre with respect to more traditional ones, to emerging topics in computational social science such as the identification of factors involved in the success of a story. The latter is the focus of the presented case-study, in which a wide set of multi-level linguistic features has been automatically extracted from a subset of the corpus and analysed in order to detect the ones which significantly discriminate successful from unsuccessful stories.

Introduction
Computational Sociolinguistics is an emergent interdisciplinary field aimed at exploiting computational approaches to study the relationship between language and society AUTHOR. One of the primary factors driving its foundation is the widespread diffusion of social media and other user-generated data available online, which has promoted massive research on computer-mediated communication from several perspectives. For instance, scholars working in the field of genre and register variation have relied on quantitative approaches to inspect the peculiarities of social media language, with the purpose of providing a characterization of this new genre with respect to more traditional ones AUTHOR. In the NLP community, the writing style of user-generated data has been analyzed through computational stylometry approaches for addressing tasks broadly related to author profiling AUTHOR, such as gender and age detection AUTHOR. The vast majority of this work has taken into account contents published on few microblogging platforms considered as more representative of the contemporary user-generated mediascape, e.g. Twitter. More recently, the attention has been oriented to the language used by online communities whose members share a common interest towards an object, an activity -- and more in general any area of human interest -- allowing scholars to shed light on the growing phenomenon of fandom AUTHOR. One of the most prominent expressions of fandom is fanfiction (fanfic, fic or FF), i.e.\ fiction written by fans of a TV series, movie, book etc., using existing characters and situations to develop new plots. In many languages dedicated websites exist where users can publish their own literary works inspired to the original book they are fans of. From a computational linguistics standpoint, one perspective from which fanfiction has been investigated aimed to infer the relationship between user-generated stories and their original source, e.g.\ comparing the representation of characters according to their gender, as well as to model reader reactions to stories AUTHOR. Inspired to that study, which was based on a large dataset of stories mainly in English, we collect a new corpus of fanfic stories}, which, to our knowledge, is the first one for the Italian language. We rely on this corpus to carry out an investigation aimed at shedding light on the possibility of computationally modeling the expected success of a fanfic story, based on the assumptions of linguistic profiling and stylometry research.

Conclusion
In this paper, we presented a NLP-based stylometric analysis on the emerging genre of fanfiction aimed at characterizing the writing style of a successful story. We collected a new large-scale corpus which – to the best of our knowledge – is the first one of this genre for Italian. We showed that successful stories, defined as those receiving a number of reviews higher that the average, are characterized by a variety of linguistic features at different levels of granularity and that these features are more uniformly distributed within them. In the future, we would like to broad the perspective to other genres in order to study whether there are linguistic predictors of successful writing which are constant across different genres, as well as across concepts somehow similar to success, such as virality and engagement.