Alternative canons: Topic modelling fanfiction reviews on Goodreads.com

Abstract
This project uses computational analysis of reviews of fanfiction texts (non-commercial works using characters, settings and plots from existing media properties) on the site Goodreads to interrogate how contemporary readers construct and discuss literary value. Fanfiction texts have typically existed outside of commercial and institutional forms of organisation and categorisation. However, the crowd-sourced book review site Goodreads allows fanfiction to be listed and reviewed along with commercially published novels. This sharing of digital space by commercial and amateur writing challenges literary boundaries and definitions. Using topic modelling – a form of machine learning – on a dataset compiled from a selection of the more than 39,000 reviews tagged as “fan fiction” on Goodreads, this project examines how readers use the terminology of canonicity and what constitutes a ‘classic’ to negotiate the place of fanfiction amongst other forms of literature and capture insight into contemporary readers’ views on literary value.
Speaker Biography
Dr Suzanne R Black received her PhD from the University of Edinburgh for doctoral work examining the interconnections of a range of literatures in the twenty-first century digital literary sphere. With a background in English Literature, she combines humanities approaches with digital methods, and has worked across a range of projects involving data and the creative industries. For more information, please see www.suzannerblack.com.
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