Design Patterns for Data-Driven News Articles
Shan Hao, Zezhong Wang, Benjamin Bach, Larissa Pschetz
Abstract
Technological advancements have resulted in great shifts in the production and consumption of news articles. This, in turn, lead to the requirement of new educational and practical frameworks. In this paper, we present a classification of data-driven news articles and related design patterns defined 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 identified 72 design patterns to understand and construct 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. Our 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.