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.

中文翻译：

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一种检测电影剧本道德冲突的图学习方法

道德冲突是吸引人的叙事的核心，但尚无从规模上从叙事中以计算方式提取道德冲突的方法。在本文中，我们提出了一种将社交网络分析和自然语言处理工具与直觉道德和榜样模型的最新理论进展相结合的方法。这种方法是根据角色之间动态演变的关系网络来考虑叙事。我们使用此方法来分析包含82,195个场景的894个电影脚本，这表明包含中心角色之间的道德冲突的场景可以使用网络模块之间的连接模式更改来识别。此外，我们导出了用于规范道德冲突衡量标准的计算模型。我们的结果表明，这种方法可以从各种各样的电影剧本中准确地提取道德冲突。我们在理论上将我们的方法整合到更大的叙事和娱乐研究环境中，阐明了计算通信研究与媒体心理学的交汇处的未来研究轨迹。