Supporting Effective and Trustworthy Data Communication through Interactive Authoring and Assessment of Data-Driven Narratives
Author(s)
Fu, Yu
Advisor(s)
Editor(s)
Collections
Supplementary to:
Permanent Link
Abstract
Data-driven narratives, which combine visualization and text to communicate quantitative insights, play an increasingly important role in how the public understands complex issues. As these narratives become more widespread, ensuring their clarity, accuracy, and trustworthiness remains a significant challenge, especially in high-stakes domains such as journalism, public health, and politics. These challenges are compounded by evolving newsroom workflows, fragmented authoring tools, and the rapid rise of generative AI.
This dissertation addresses these concerns through a combination of ecosystem analysis, empirical investigation, and interactive system design. It begins with a study of real-world data journalism practices, identifying mismatches between the needs of practitioners and the ways computational tools are typically designed and evaluated in visualization and HCI research. Building on this foundation, I curate and analyze a diverse set of problematic data narratives to develop a multi-dimensional taxonomy of common issues, which are then mapped onto a structured data communication pipeline. To support improved practices, I present two interactive systems. The first, DataWeaver, is an authoring tool that supports composing visualization and text through a bidirectional workflow, helping authors maintain alignment and accuracy during narrative construction. The second, Aletheia, supports fact-checking by connecting data claims to structured evidence using large language models, paired with interactive explanations to guide verification.
Together, these contributions integrate conceptual insight, human-centered design, technological innovation, and empirical evaluation to promote more transparent, accurate, and responsible data communication.
Sponsor
Date
2025-08-22
Extent
Resource Type
Text
Resource Subtype
Dissertation (PhD)