Hits or Misses? A Linguistically Explainable Formula for Fanfiction Success
Authors: Giulio Leonardi, Dominique Brunato, Felice Dell'Orletta (Università di Pisa, CNR-ILC, 2024)
Topic: Textual Genres & Literature Linguistics

Abstract
This study presents a computational analysis of Italian fanfiction, aiming to construct an interpretable model of successful writing within this emerging literary domain. Leveraging explicit features that capture both linguistic style and semantic content, we demonstrate the feasibility of automatically predicting successful writing in fanfiction and we identify a set of robust linguistic predictors that maintain their predictive power across diverse topics and time periods, offering insights into the universal aspects of engaging storytelling. This approach not only enhances our understanding of fanfiction as a genre but also offers potential applications in broader literary analysis and content creation.

Introduction
The growing proliferation of online literary content has led to the emergence of new genres and storytelling forms, with fanfiction being particularly popular among teens and young adults. Fanfiction consists of stories created by fans (mostly hobby authors) that extend or alter the narrative of existing popular media like books, movies, comics or games, and represents a significant portion of user-generated content on the web AUTHOR. In recent years, the widespread popularity that this genre has assumed has prompted research into the linguistic and stylistic elements that contribute to its success, %remain largely unexplored from a linguistic perspective, particularly in contrast mirroring studies conducted on more traditional literary genres AUTHOR, among others. Understanding the elements that contribute to narrative success is a fascinating area of research with implications across various fields, from literary analysis to digital humanities. From a socio-linguistic perspective, it can offer deeper insights into people and culture. It also has significant applications in areas such as personalized content recommendation and educational technology AUTHOR. While personal interests undoubtedly play a crucial role in predicting a reader's engagement with a literary content, the way information is presented can also evoke different reactions and levels of interaction, ultimately influencing the narrative's success. In this regards, recent advancements in Natural Language Processing (NLP) and machine learning offer a powerful lens for making explicit patterns that may explain the complex interplay between reader engagement and content success. This paper moves in this field and presents a computational analysis focused on Italian fanfiction, addressing the following research questions: i.) Can the success of Italian fanfiction be automatically predicted using stylistic and lexical features of the texts?; ii.) Which types of features demonstrate the highest predictive capability, and how consistent are these features across different time periods and thematic domains?; iii.) To what extent can these features be explained in terms of their contribution to predicting success? Our contributions. i.) We collected a corpus of Italian fanfiction stories enriched with metadata considered as proxies of their success; %; ii.) We investigate the relationship between stylistic and lexical features of stories and their success from a modeling perspective; iii.) We identified the most influential features in success prediction, showing the key role played by form and stylistic related features across time and thematic domains of fanfictions. The paper is structured as follows: Section sec:related_work briefly contextualizes our study among relevant literature; Section sec:corpus presents the reference corpus of Italian fanfiction stories that we collected; in Section sec:approach we provide an overview of the approach we devised including the description of features used for classification and the classifiers employed. Section sec:results discusses the main findings and offers a fine-grained analysis of the classification results in terms of feature explainability. In Section sec:conclusion we summarize key findings and outlining promising directions for future research in this field.

Conclusion
Understanding success factors in literary writing is an evolving area of cross-disciplinary research. This study on Italian fanfiction demonstrated the feasibility of predicting success using computational methods and explainability techniques. Notably, we found that features related to style and structure of texts show greater robustness than lexical ones across different domains and time periods. This suggests that the way a story is crafted may be more universally appealing than specific word choices or thematic elements. We believe that the implications of this study extend far beyond fanfiction research. On the one hand, it provides new methodologies for analyzing online literary phenomena offering potential contributions to digital humanities. From the NLP perspective, it could inform text generation models, potentially guiding the creation of content that resonates more effectively with readers. %to the growing field of digital humanities, offering new methodologies for analyzing and understanding online literary phenomena. From an NLP perspective, our findings can inform the development of more sophisticated text generation models, potentially guiding the creation of content that resonates more effectively with readers. Future research could explore the generalizability of these findings to other languages and genres, as well as the investigation on the dynamics of evolving reader preferences over time by also considering alternative measures to gauge success. Additionally, this study does not take into account the importance of the author; a potential future development would be to consider the impact of the author's popularity and productivity on the success of their fanfiction. \newpage \appendix