A Graph-Learning Approach for Detecting Moral Conflict in Movie Scripts

Open Access Journal | ISSN: 2183-2439

Article | Open Access

A Graph-Learning Approach for Detecting Moral Conflict in Movie Scripts


  • Frederic René Hopp Media Neuroscience Lab, Department of Communication, University of California Santa Barbara, USA
  • Jacob Taylor Fisher Media Neuroscience Lab, Department of Communication, University of California Santa Barbara, USA
  • René Weber Media Neuroscience Lab, Department of Communication, University of California Santa Barbara, USA


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Abstract:  Moral conflict is central to appealing narratives, but no methodology exists for computationally extracting moral conflict from narratives at scale. In this article, we present an approach combining tools from social network analysis and natural language processing with recent theoretical advancements in the Model of Intuitive Morality and Exemplars. This approach considers narratives in terms of a network of dynamically evolving relationships between characters. We apply this method in order to analyze 894 movie scripts encompassing 82,195 scenes, showing that scenes containing moral conflict between central characters can be identified using changes in connectivity patterns between network modules. Furthermore, we derive computational models for standardizing moral conflict measurements. Our results suggest that this method can accurately extract moral conflict from a diverse collection of movie scripts. We provide a theoretical integration of our method into the larger milieu of storytelling and entertainment research, illuminating future research trajectories at the intersection of computational communication research and media psychology.

Keywords:  computational narratology; entertainment; eMFD; graph learning; MIME; moral conflict; movie scripts; network science

Published:   13 August 2020


DOI: https://doi.org/10.17645/mac.v8i3.3155


© Frederic Rene Hopp, Jacob Taylor Fisher, Rene Weber. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 license (http://creativecommons.org/licenses/by/4.0), which permits any use, distribution, and reproduction of the work without further permission provided the original author(s) and source are credited.