TY - JOUR
T1 - ChartStory
T2 - Automated Partitioning, Layout, and Captioning of Charts into Comic-Style Narratives
AU - Zhao, Jian
AU - Xu, Shenyu
AU - Chandrasegaran, Senthil
AU - Bryan, Chris
AU - Du, Fan
AU - Mishra, Aditi
AU - Qian, Xin
AU - Li, Yiran
AU - Ma, Kwan Liu
N1 - Publisher Copyright:
© 1995-2012 IEEE.
PY - 2023/2/1
Y1 - 2023/2/1
N2 - Visual data storytelling is gaining importance as a means of presenting data-driven information or analysis results, especially to the general public. This has resulted in design principles being proposed for data-driven storytelling, and new authoring tools being created to aid such storytelling. However, data analysts typically lack sufficient background in design and storytelling to make effective use of these principles and authoring tools. To assist this process, we present ChartStory for crafting data stories from a collection of user-created charts, using a style akin to comic panels to imply the underlying sequence and logic of data-driven narratives. Our approach is to operationalize established design principles into an advanced pipeline that characterizes charts by their properties and similarities to each other, and recommends ways to partition, layout, and caption story pieces to serve a narrative. ChartStory also augments this pipeline with intuitive user interactions for visual refinement of generated data comics. We extensively and holistically evaluate ChartStory via a trio of studies. We first assess how the tool supports data comic creation in comparison to a manual baseline tool. Data comics from this study are subsequently compared and evaluated to ChartStory's automated recommendations by a team of narrative visualization practitioners. This is followed by a pair of interview studies with data scientists using their own datasets and charts who provide an additional assessment of the system. We find that ChartStory provides cogent recommendations for narrative generation, resulting in data comics that compare favorably to manually-created ones.
AB - Visual data storytelling is gaining importance as a means of presenting data-driven information or analysis results, especially to the general public. This has resulted in design principles being proposed for data-driven storytelling, and new authoring tools being created to aid such storytelling. However, data analysts typically lack sufficient background in design and storytelling to make effective use of these principles and authoring tools. To assist this process, we present ChartStory for crafting data stories from a collection of user-created charts, using a style akin to comic panels to imply the underlying sequence and logic of data-driven narratives. Our approach is to operationalize established design principles into an advanced pipeline that characterizes charts by their properties and similarities to each other, and recommends ways to partition, layout, and caption story pieces to serve a narrative. ChartStory also augments this pipeline with intuitive user interactions for visual refinement of generated data comics. We extensively and holistically evaluate ChartStory via a trio of studies. We first assess how the tool supports data comic creation in comparison to a manual baseline tool. Data comics from this study are subsequently compared and evaluated to ChartStory's automated recommendations by a team of narrative visualization practitioners. This is followed by a pair of interview studies with data scientists using their own datasets and charts who provide an additional assessment of the system. We find that ChartStory provides cogent recommendations for narrative generation, resulting in data comics that compare favorably to manually-created ones.
KW - Data story generation
KW - data comics
KW - data-driven storytelling
KW - narrative visualization
UR - http://www.scopus.com/inward/record.url?scp=85115699893&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85115699893&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2021.3114211
DO - 10.1109/TVCG.2021.3114211
M3 - Article
C2 - 34559655
AN - SCOPUS:85115699893
SN - 1077-2626
VL - 29
SP - 1384
EP - 1399
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 2
ER -