Big data meets storytelling: using machine learning to predict popular fanfiction Article Swipe
Duy Nguyen
,
Stephen Zigmond
,
Samuel Glassco
,
Bach Xuan Tran
,
Philippe J. Giabbanelli
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s13278-024-01224-x
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s13278-024-01224-x
Fanfictions are a popular literature genre in which writers reuse a universe, for example to transform heteronormative relationships with queer characters or to bring romance into shows focused on horror and adventure. Fanfictions have been the subject of numerous studies in text mining and network analysis, which used Natural Language Processing (NLP) techniques to compare fanfictions with the original scripts or to make various predictions. In this paper, we use NLP to predict the popularity of a story and examine which features contribute to popularity. This endeavor is important given the rising use of AI assistants and the ongoing interest in generating text with desirable characteristics. We used the main two websites to collect fan stories (Fanfiction.net and Archives Of Our Own) on Supernatural, which has been the subject of numerous scholarly works. We extracted high-level features such as the main character and sentiments from 79,288 of these stories and used the features in a binary classification supported by tree-based methods, ensemble methods (random forest), neural networks, and Support Vector Machines. Our optimized classifiers correctly identified popular stories in four out of five cases. By relating features to classification outcomes using SHAP values, we found that fans prefer longer stories with a wider vocabulary, which can inform the prompts of AI chatbots to continue generating such successful stories. However, we also observed that fans wanted stories unlike the original material (e.g., favoring romance and disliking when characters are hurt), hence AI-powered stories may be less popular if they strictly follow the original material of a show.
Related Topics
Concepts
Popularity
Artificial intelligence
Computer science
Scripting language
Queer
Adventure
Romance
Subject (documents)
Natural language processing
Character (mathematics)
Storytelling
Support vector machine
Machine learning
World Wide Web
Narrative
Literature
Psychology
Art
Psychoanalysis
Operating system
Social psychology
Mathematics
Geometry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s13278-024-01224-x
- https://link.springer.com/content/pdf/10.1007/s13278-024-01224-x.pdf
- OA Status
- hybrid
- Cited By
- 3
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392860805
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392860805Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s13278-024-01224-xDigital Object Identifier
- Title
-
Big data meets storytelling: using machine learning to predict popular fanfictionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-15Full publication date if available
- Authors
-
Duy Nguyen, Stephen Zigmond, Samuel Glassco, Bach Xuan Tran, Philippe J. GiabbanelliList of authors in order
- Landing page
-
https://doi.org/10.1007/s13278-024-01224-xPublisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s13278-024-01224-x.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s13278-024-01224-x.pdfDirect OA link when available
- Concepts
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Popularity, Artificial intelligence, Computer science, Scripting language, Queer, Adventure, Romance, Subject (documents), Natural language processing, Character (mathematics), Storytelling, Support vector machine, Machine learning, World Wide Web, Narrative, Literature, Psychology, Art, Psychoanalysis, Operating system, Social psychology, Mathematics, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- References (count)
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52Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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