"I Came Across a Junk": Understanding Design Flaws of Data Visualization from the Public's Perspective

The visualization community has a rich history of reflecting upon visualization design flaws. Although research in this area has remained lively, we believe it is essential to continuously revisit this classic and critical topic in visualization research by incorporating more empirical evidence from diverse sources, characterizing new design flaws, building more systematic theoretical frameworks, and understanding the underlying reasons for these flaws. To address the above gaps, this work investigated visualization design flaws through the lens of the public, constructed a framework to summarize and categorize the identified flaws, and explored why these flaws occur. Specifically, we analyzed 2227 flawed data visualizations collected from an online gallery and derived a design task-associated taxonomy containing 76 specific design flaws. These flaws were further classified into three high-level categories (i.e., misinformation, uninformativeness, unsociability) and ten subcategories (e.g., inaccuracy, unfairness, ambiguity). Next, we organized five focus groups to explore why these design flaws occur and identified seven causes of the flaws. Finally, we proposed a research agenda for combating visualization design flaws and summarize nine research opportunities.