Dr Suzanne R. Black

Digital Research Postdoctoral Fellow
Dr Suzanne R Black

Dr Suzanne R. Black – https://orcid.org/0000-0001-5479-0528

Digital Research Postdoctoral Fellow, April – June 2024

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. She has held the position of Research Fellow in Data Service Design and, since then, has continued to work as postdoctoral researcher in Creative Informatics at the University of Edinburgh. 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. Her research interests lie in digital literary culture and the development of data-led approaches to contemporary fiction and fanfiction. She has published in Queer Studies in Media & Popular Culture and Transformative Works and Cultures. For more information, please see www.suzannerblack.com.

Project title: The reviews are in: A computational study of fanfiction, canonicity and literary value on Goodreads

My research attends to interconnections in the digital literary sphere where commercially published novels and born-digital amateur fiction share the same spaces along with online book retailers, review sites and discourses on literary topics. 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 ask 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 39,054 works tagged as “fan fiction” on Goodreads, this project will examine 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. Additionally, this research contributes to the insights that can be gained from combining computational methods and internet data to explore how algorithmic culture participates in literary reception.