  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.
