In this paper, we propose StoryPrint, an interactive visualization of creative storytelling that facilitates individual and comparative structural analyses. This visualization method is intended for script-based media, which has suitable metadata. The pre-visualization process involves parsing the script into different metadata categories and analyzing the sentiment on a character and scene basis. For each scene, the setting, character presence, character prominence, and character emotion of a film are represented as a StoryPrint. The visualization is presented as a radial diagram of concentric rings wrapped around a circular time axis. A user then has the ability to toggle a difference overlay to assist in the cross-comparison of two different scene inputs.

We present a novel approach to modeling stories using recurrent neural networks. Different story features are extracted using natural language processing techniques and used to encode the stories as sequences. These sequences can be learned by deep neural networks, in order to predict the next story events. The predictions can be used as an inspiration for writers who experience a writer's block. We further assist writers in their creative process by generating visualizations of the character interactions in the story. We show that suggestions from our model are rated as highly as the real scenes from a set of films and that our visualizations can help people in gaining deeper story understanding.

In order to assist scriptwriters during the process of story-writing, we have developed a system that can extract information from natural language stories, and allow for story-centric as well as character-centric reasoning. These inferencing capabilities are exposed to the user through intuitive querying systems, allowing the scriptwriter to ask the system questions about story and character information. We introduce knowledge bytes as atoms of information and demonstrate that the system can parse text into a stream of knowledge bytes and use these mentioned reasoning capabilities through logical reasoning.

We present Cardinal, a tool for computer-assisted authoring of movie scripts. Cardinal provides a means of viewing a script through a variety of perspectives, for interpretation as well as editing. This is made possible by virtue of intelligent automated analysis of natural language scripts and generating different intermediate representations. Cardinal generates 2-D and 3-D visualizations of the scripted narrative and also presents interactions in a timeline-based view. The visualizations empower the scriptwriter to understand their story from a spatial perspective, and the timeline view provides an overview of the interactions in the story. The user study reveals that users of the system demonstrated confidence and comfort using the system.

In this paper, we present story curves, a visualization technique for exploring and communicating nonlinear narratives in movies. A nonlinear narrative is a storytelling device that portrays events of a story out of chronological order, e.g., in reverse order or going back and forth between past and future events. Many acclaimed movies employ unique narrative patterns which in turn have inspired other movies and contributed to the broader analysis of narrative patterns in movies. However, understanding and communicating nonlinear narratives is a difficult task due to complex temporal disruptions in the order of events as well as no explicit records specifying the actual temporal order of the underlying story. Story curves visualize the nonlinear narrative of a movie by showing the order in which events are told in the movie and comparing them to their actual chronological order, resulting in possibly meandering visual patterns in the curve. We also present Story Explorer, an interactive tool that visualizes a story curve together with complementary information such as characters and settings. Story Explorer further provides a script curation interface that allows users to specify the chronological order of events in movies. We used Story Explorer to analyze 10 popular nonlinear movies and describe the spectrum of narrative patterns that we discovered, including some novel patterns not previously described in the literature. Feedback from experts highlights potential use cases in screenplay writing and analysis, education and film production. A controlled user study shows that users with no expertise are able to understand visual patterns of nonlinear narratives using story curves.