The Style of a Successful Story - A Computational Study on the Fanfiction Genre

Aug 27, 2026

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The study introduces a new Italian-language corpus representative of the fanfiction genre, consisting of roughly 55k user-generated stories inspired by the fantasy saga “Harry Potter”. The corpus supports data-driven analyses ranging from genre characterization through linguistic variation to computational social science questions. A focused case study extracts multi-level linguistic features from a corpus subset and analyzes them to identify the features that significantly distinguish successful from unsuccessful stories, enabling computational modeling of fanfic success.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Abstract

Introduction

Dataset collection

The Style of a Successful Story: a Computational Study on the Fanﬁction Genre Andrea Mattei􀀏 , Dominique Brunato􀀅 , Felice Dell'Orletta􀀅 􀀏 University of Pisa [a.mattei3@studenti.unipi.it](a.mattei3@studenti.unipi.it) 􀀅 Istituto di Linguistica Computazionale “Antonio Zampolli”(ILC–CNR) ItaliaNLP Lab-[www.italianlp.it](www.italianlp.it) fdominique.brunato, [felice.dellorletta](felice.dellorlettag@ilc.cnr.it)[g](felice.dellorlettag@ilc.cnr.it)[@ilc.cnr.it](felice.dellorlettag@ilc.cnr.it) 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 asthe 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 1 Introduction Computational Sociolinguistics is an emergent interdisciplinary ﬁeld aimed at exploiting computational approaches to study the relationship between language and society (Nguyen et al., 2016) . 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 Copyright 􀀍c 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4 .0) . a characterization of this new genre with respect to more traditional ones (Paolillo, 2001; Herring and Androutsopoulos, 2015) . 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 (Daelemans, 2013), such as gender and age detection (Peersman et al., 2011; Koppel et al., 2002) . 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 (Sindoni, 2015) . 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 (Smitha and Bamman, 2016) . Inspired to that study, which was based on a large dataset of stories mainly in English, we collect anew corpus offanﬁc stories 1 , which, to our knowledge, is the ﬁrst one for the Italian language. We rely on this corpus to carry out an investiga

FAQs

What is the main contribution of the paper?

The paper presents a new Italian-language fanfiction corpus and uses it for an investigation into what linguistic factors correlate with story success.

How was the dataset collected?

Texts were collected from efpfanfic.net using web scraping with two Python spiders built on the Scrapy framework, downloading story chapters and associated metadata.

What does the case study analyze to measure story success?

It automatically extracts multi-level linguistic features from a subset of the corpus and analyzes which features significantly discriminate successful from unsuccessful fanfiction stories.

Aug 27, 2026

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