Composing Data Stories with Meta Relations
Haotian Li, Lu Ying, Leixian Shen, Yun Wang, Yingcai Wu, Huamin Qu

TL;DR
This paper explores how incorporating meta relations, which go beyond dataset information, can enhance AI-generated data stories by aligning more closely with human storytelling practices, through a user study with the Remex tool.
Contribution
It introduces the concept of meta relations in data storytelling and demonstrates their potential benefits through an exploratory user study with an AI tool.
Findings
Meta relations improve the quality of data stories.
Diverse usage scenarios for meta relations were identified.
AI can effectively suggest and apply meta relations in storytelling.
Abstract
To facilitate the creation of compelling and engaging data stories, AI-powered tools have been introduced to automate the three stages in the workflow: analyzing data, organizing findings, and creating visuals. However, these tools rely on data-level information to derive inflexible relations between findings. Therefore, they often create one-size-fits-all data stories. Differently, our formative study reveals that humans heavily rely on meta relations between these findings from diverse domain knowledge and narrative intent, going beyond datasets, to compose their findings into stylized data stories. Such a gap indicates the importance of introducing meta relations to elevate AI-created stories to a satisfactory level. Though necessary, it is still unclear where and how AI should be involved in working with humans on meta relations. To answer the question, we conducted an exploratory…
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Taxonomy
TopicsSemantic Web and Ontologies · Data Visualization and Analytics · Scientific Computing and Data Management
