Composing Data Stories with Meta Relations
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910775453417472 |
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| author | Li, Haotian Ying, Lu Shen, Leixian Wang, Yun Wu, Yingcai Qu, Huamin |
| author_facet | Li, Haotian Ying, Lu Shen, Leixian Wang, Yun Wu, Yingcai Qu, Huamin |
| contents | 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 user study with Remex, an AI-powered data storytelling tool that suggests meta relations in the analysis stage and applies meta relations for data story organization. The user study reveals various findings about introducing AI for meta relations into the storytelling workflow, such as the benefit of considering meta relations and their diverse expected usage scenarios. Finally, the paper concludes with lessons and suggestions about applying meta relations to compose data stories to hopefully inspire future research. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_03603 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Composing Data Stories with Meta Relations Li, Haotian Ying, Lu Shen, Leixian Wang, Yun Wu, Yingcai Qu, Huamin Human-Computer Interaction 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 user study with Remex, an AI-powered data storytelling tool that suggests meta relations in the analysis stage and applies meta relations for data story organization. The user study reveals various findings about introducing AI for meta relations into the storytelling workflow, such as the benefit of considering meta relations and their diverse expected usage scenarios. Finally, the paper concludes with lessons and suggestions about applying meta relations to compose data stories to hopefully inspire future research. |
| title | Composing Data Stories with Meta Relations |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2501.03603 |