Narratives serve as fundamental frameworks in our understanding of the world and play a crucial role in collaborative sensemaking, providing a versatile foundation for sensemaking. Framing is a subtle yet potent mechanism that influences public perception through specific word choices, shaping interpretations of reported news events. Despite the recognized importance of narratives and framing, a significant gap exists in the literature with regard to the explicit consideration of framing within the context of computational extraction and representation. This article explores the capabilities of a specific narrative extraction and representation approach -- narrative maps -- to capture framing information from news data. The research addresses two key questions: (1) Does the narrative extraction method capture the framing distribution of the data set? (2) Does it produce a representation with consistent framing? Our results indicate that while the algorithm captures framing distributions, achieving consistent framing across various starting and ending events poses challenges. Our results highlight the potential of narrative maps to provide users with insights into the intricate framing dynamics within news narratives. However, we note that directly leveraging framing information in the computational narrative extraction process remains an open challenge.

翻译：叙事作为我们理解世界的基础框架，在协同意义建构中发挥着关键作用，为意义建构提供了多功能基础。框架是一种微妙而强大的机制，通过特定词汇选择影响公众认知，塑造对新闻报道事件的解读。尽管叙事与框架的重要性已得到公认，但在涉及叙事计算提取与表征的文献中，明确考虑框架的研究仍存在显著空白。本文探讨了一种特定叙事提取与表征方法——叙事图谱——从新闻数据中捕获框架信息的能力。本研究聚焦两个关键问题：（1）叙事提取方法能否捕获数据集的框架分布？（2）该方法能否生成具有一致性框架的表征？结果表明，尽管算法能够捕获框架分布，但在不同起始事件与结束事件之间实现框架一致性仍存在挑战。我们的研究揭示了叙事图谱在帮助用户理解新闻叙事中复杂框架动态方面的潜力，但需要指出的是，在计算叙事提取过程中直接利用框架信息仍是一个待解决的开放性难题。