Suzanne R. Black

Dr. Suzanne R. Black is a Postdoctoral Research Associate at the University of Edinburgh, working within the CoSTAR Foresight Lab. Her research sits at the intersection of humanities inquiry and computational methods, focusing on digital literary culture, fanfiction, and creative industries data analysis. She holds a PhD from the University of Edinburgh where her doctoral work examined 21st-century digital literary networks.

• PhD in English Literature, University of Edinburgh

Her research interests center on digital literary culture, fanfiction archives, and queer/feminist theories applied to contemporary reading practices. She pioneers computational approaches like topic modeling to analyze large-scale datasets from platforms such as Goodreads, investigating how readers construct literary value and negotiate canonicity in digital spaces. Her work reveals how fan communities dynamically define 'classics' through emotional engagement and collective discourse.

Recent publications demonstrate a cohesive trajectory in cultural analytics and digital literary studies, blending computational techniques with critical theory to examine pandemic impacts on festivals, NFT ownership models, and DEI initiatives in creative sectors. A recurring theme is the tension between algorithmic systems and human-centered cultural production.

• Fellowship at the Institute for Advanced Studies in the Humanities

Dr. Black has secured research funding through institutional fellowships supporting her Goodreads fanfiction analysis. While not formally advising graduate students, she actively shapes academic discourse through invited lectures at Newcastle University, the University of Edinburgh, and Porty Pride events. Her grant-funded projects often involve cross-disciplinary collaboration with data scientists and social researchers.

As a core member of the CoSTAR Foresight Lab, she contributes to the Edinburgh Futures Institute's mission of data-driven creative industry innovation. Her work integrates digital humanities methodologies with cultural analytics to address structural biases in digital heritage preservation and fanfiction archiving.