A Computational Analysis of Inter-Authorial Influence Within Fanfiction Communities

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Abstract/Contents

Abstract
Although fanfiction is built on existing media properties, its reliance on shared worldbuilding and inspiration makes patterns of influence much more apparent within given communities than in other media environments; some call this derivative. Rather than debating the merits or weaknesses of fanfiction or making value judgments regarding its supposedly derivative nature, this project explores the role that influence — in particular, inter-authorial influence — plays in fanfiction production. Within literary communities where every work is already explicitly based upon one common world or group of characters, what does it mean when a fanfiction author cites a separate piece of fanfiction as inspiration for their work? Is this author’s work more similar to that one fanfic that inspired it than to every other fanfic centered around the same world or characters? My hypothesis is that fanfics that cite another fanfic as their inspiration will be more similar to their inspiration fanfic than to other fanfics within the same fandom in both in terms of the words used in the stories and in terms of the overall emotional arcs of the stories. This project explores whether fanfic pairings in which one work was cited as having inspired the other are more similar to each other than fanfic pairings within the same fandom but without any noted influence according to the metrics of cosine similarity and sentiment-arc variance.

Description

Type of resource text
Date created August 31, 2023
Publication date December 8, 2023; August 31, 2023

Creators/Contributors

Author Johnson, Natasha

Subjects

Subject fanfiction
Subject Cosine Similarity
Subject Natural language processing (Computer science)
Subject Influence (Literary, artistic, etc.)
Subject Fan magazines
Genre Text
Genre Thesis

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User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
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This work is licensed under a Creative Commons Zero v1.0 Universal license (CC0).

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Preferred citation
Johnson, N. (2023). A Computational Analysis of Inter-Authorial Influence Within Fanfiction Communities. Stanford Digital Repository. Available at https://purl.stanford.edu/zm212kh6889. https://doi.org/10.25740/zm212kh6889.

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Undergraduate Honors Theses, Symbolic Systems Program, Stanford University

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