dc.contributor.author	Keith Norambuena, Brian Felipe	en
dc.contributor.committeechair	North, Christopher L.	en
dc.contributor.committeechair	Mitra, Tanushree	en
dc.contributor.committeemember	Fox, Edward A.	en
dc.contributor.committeemember	Horning, Michael A.	en
dc.contributor.committeemember	Ramakrishnan, Narendran	en
dc.contributor.department	Computer Science and Applications	en
dc.date.accessioned	2023-08-09T08:00:26Z	en
dc.date.available	2023-08-09T08:00:26Z	en
dc.date.issued	2023-08-08	en
dc.description.abstract	Narratives are fundamental to our understanding of the world, and they are pervasive in all activities that involve representing events in time. Narrative analysis has a series of applications in computational journalism, intelligence analysis, and misinformation modeling. In particular, narratives are a key element of the sensemaking process of analysts. In this work, we propose a narrative model and visualization method to aid analysts with this process. In particular, we propose the narrative maps framework—an event-based representation that uses a directed acyclic graph to represent the narrative structure—and a series of empirically defined design guidelines for map construction obtained from a user study. Furthermore, our narrative extraction pipeline is based on maximizing coherence—modeled as a function of surface text similarity and topical similarity—subject to coverage—modeled through topical clusters—and structural constraints through the use of linear programming optimization. For the purposes of our evaluation, we focus on the news narrative domain and showcase the capabilities of our model through several case studies and user evaluations. Moreover, we augment the narrative maps framework with interactive AI techniques—using semantic interaction and explainable AI—to create an interactive narrative model that is capable of learning from user interactions to customize the narrative model based on the user's needs and providing explanations for each core component of the narrative model. Throughout this process, we propose a general framework for interactive AI that can handle similar models to narrative maps—that is, models that mix continuous low-level representations (e.g., dimensionality reduction) with more abstract high-level discrete structures (e.g., graphs). Finally, we evaluate our proposed framework through an insight-based user study. In particular, we perform a quantitative and qualitative assessment of the behavior of users and explore their cognitive strategies, including how they use the explainable AI and semantic interaction capabilities of our system. Our evaluation shows that our proposed interactive AI framework for narrative maps is capable of aiding users in finding more insights from data when compared to the baseline.	en
dc.description.abstractgeneral	Narratives are essential to how we understand the world. They help us make sense of events that happen over time. This research focuses on developing a method to assist people, like journalists and analysts, in understanding complex information. To do this, we introduce a new approach called narrative maps. This model allows us to extract and visualize stories from text data. To improve our model, we use interactive artificial intelligence techniques. These techniques allow our model to learn from user feedback and be customized to fit different needs. We also use these methods to explain how the model works, so users can understand it better. We evaluate our approach by studying how users interact with it when doing a task with news stories. We consider how useful the system is in helping users gain insights. Our results show that our method aids users in finding important insights compared to traditional methods.	en
dc.description.degree	Doctor of Philosophy	en
dc.format.medium	ETD	en
dc.identifier.other	vt_gsexam:38146	en
dc.identifier.uri	http://hdl.handle.net/10919/116006	en
dc.language.iso	en	en
dc.publisher	Virginia Tech	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Narratives	en
dc.subject	Visual Analytics	en
dc.subject	Semantic Interaction	en
dc.subject	Interactive AI	en
dc.title	Narrative Maps: A Computational Model to Support Analysts in Narrative Sensemaking	en
dc.type	Dissertation	en
thesis.degree.discipline	Computer Science and Applications	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	doctoral	en
thesis.degree.name	Doctor of Philosophy	en

Narrative Maps: A Computational Model to Support Analysts in Narrative Sensemaking

Files

Original bundle

Collections