From 'Wow' to 'Why': Guidelines for Creating the Opening of a Data Video with Cinematic Styles

Xian XU, Leni YANG, David YIP, Mingming FAN*, Zheng WEI, Huamin QU

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

20 Citations (Scopus)

Abstract

Data videos are an increasingly popular storytelling form. The opening of a data video critically influences its success as the opening either attracts the audience to continue watching or bores them to abandon watching. However, little is known about how to create an attractive opening. We draw inspiration from the openings of famous films to facilitate designing data video openings. First, by analyzing over 200 films from several sources, we derived six primary cinematic opening styles adaptable to data videos. Then, we consulted eight experts from the film industry to formulate 28 guidelines. To validate the usability and effectiveness of the guidelines, we asked participants to create data video openings with and without the guidelines, which were then evaluated by experts and the general public. Results showed that the openings designed with the guidelines were perceived to be more attractive, and the guidelines were praised for clarity and inspiration.
Original languageEnglish
Title of host publicationCHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
Number of pages20
ISBN (Electronic)9781450391573
DOIs
Publication statusPublished - 29 Apr 2022
Externally publishedYes
Event2022 CHI Conference on Human Factors in Computing Systems - New Orleans, United States
Duration: 29 Apr 20225 May 2022

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2022 CHI Conference on Human Factors in Computing Systems
Abbreviated titleCHI 2022
Country/TerritoryUnited States
CityNew Orleans
Period29/04/225/05/22

Bibliographical note

Publisher Copyright:
© 2022 ACM.

Funding

This research was supported in part by Hong Kong Theme-based Research Scheme T41-709/17N.

Keywords

  • Data Video
  • Guideline
  • Interview
  • Lab Study
  • Storytelling
  • Visualization

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