Design patterns for data-driven news articles

Shan Hao, Zezhong Wang, Benjamin Bach, Larissa Pschetz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Technological advancements have resulted in great shifts in the production and consumption of news articles, which leads to the need to develop new educational and practical frameworks. This paper presents a classification of data-driven news articles and presents patterns to describe their visual and textual components. Through the analysis of 162 data-driven news articles collected from news media, we identified five types of articles based on the level of data involvement and narrative complexity: Quick Update, Briefing, Chart Description, Investigation, and In-depth Investigation. We then developed 72 design patterns to support the understanding and construction of data-driven news articles. To evaluate this approach, we conducted workshops with 23 students from journalism, design, and sociology who were newly introduced to the subject. Findings suggest that our approach can be used as an out-of-box framework for the formulation of plans and consideration of details in the workflow of data-driven news creation.
Original languageEnglish
Title of host publicationCHI'24
Subtitle of host publicationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems
EditorsFlorian Floyd Mueller, Corina Sas, Penny Kyburz, Julie R Williamson, Max L Wilson, Phoebe Toups Dugas, Irina Shklovski
Number of pages16
ISBN (Electronic)9798400703300
Publication statusPublished - 11 May 2024

Keywords / Materials (for Non-textual outputs)

  • design patterns
  • data journalism
  • data-driven storytelling
  • education
  • classification


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