Beyond Initial Creation: Two Decades of Community-based Chart Revisions from Wikimedia Commons

Authors

Jasmine Y Shih (University of California, Berkeley), Chase Stokes (Morningstar, Inc.), Melanie Tory (Northeastern University), Marti Hearst (UC Berkeley)

Presentation

Session: Data, Meet Human: Vis That Cares
Time: Wednesday, Nov 11, 10:45 – 10:54 (US/Eastern) · session 10:00 – 11:30
Location: Hall America north

Keywords

Community Editing, Chart Edit History, Wikimedia

Abstract

Open online communities like Wikipedia offer a rich space for understanding the organic evolution of public-facing artifacts, but little is known about how these communities collectively edit chart images. We investigate the phenomenon of "community editing" of visualizations by exploring chart revision patterns on Wikimedia Commons. We introduce a novel dataset of 7,786 longitudinal chart revisions across 1,664 images, labeled with edit categories using an LLM pipeline. Initial data analysis uncovered several novel findings. Multi-contributor revision is associated with more edits to mark styling and text content compared to single-contributor revision. Temporally, mark and text edits occur in more "bursty" cadences than data edits. In addition, while SVG charts attract more unique contributors and revisions than raster charts, their edit histories involve more tightly scoped and less diverse changes. This dataset enables a wide range of analyses of chart change over time.

For Practitioners

Contributors to Wikimedia content, developers for collaborative media editors, and designers of data visualization would be interested in reading this paper. For instance, multimedia content developers can prioritize or even enforce the use of SVG formats, in line with the finding that SVG charts attract more contributors and more revisions. Platform developers for collaborative editors can utilize the findings to design specialized chart-editing tools that better facilitate community-based iteration. Visualization designers working with or for a general-public audience can draw inspiration from the design patterns of Wikipedia charts.