Skip to main content
Home

Main navigation

  • Home
  • Series
  • People
  • Depts & Colleges
  • Open Education

Main navigation

  • Home
  • Series
  • People
  • Depts & Colleges
  • Open Education

Computational Literary Studies and Mental Health

Series
Textual Therapies
Audio Embed
A project combining English literature, experimental psychology, and computational linguistics, with a focus on entropy, abstraction, and mental health.
James Carney's current research investigates how mental illness interacts with textual structures – specifically, using machine learning to investigate the potential therapeutic qualities of literature with different levels of entropy (unpredictability) and abstraction, for anxiety disorders versus depression. We also touch on wider questions of motivation in the health humanities and literary studies, the appeal of belief in the transformative power of literature, and the expansion of textual/computational inquiry out into structural anthropology.

More in this series

View Series
Textual Therapies

What Does Disney do to Mental Health?

Exploring the dangers of Disney’s take on poverty, mental health, and relationships.
Previous
Licence
Creative Commons Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Episode Information

Series
Textual Therapies
People
James Carney
Emily Troscianko
Keywords
abstraction
anthropology
anxiety disorders
artificial intelligence
computational linguistics
depression
entropy
experimental psychology
literary studies
machine learning
medical humanities
mental health
psychiatry
religion
Department: The Oxford Research Centre in the Humanities (TORCH)
Date Added: 12/09/2018
Duration: 00:29:32

Subscribe

Apple Podcast Audio Audio RSS Feed

Download

Download Audio

Footer

  • About
  • Accessibility
  • Contribute
  • Copyright
  • Contact
  • Privacy
'Oxford Podcasts' Twitter Account @oxfordpodcasts | MediaPub Publishing Portal for Oxford Podcast Contributors | Upcoming Talks in Oxford | © 2011-2022 The University of Oxford