Problem
Research questions and friction points this paper is trying to address.
Wikipedia
scientific knowledge evolution
revision history
scientific edits
knowledge tracking
WikiSTAR: A System for Shedding Light on the Hidden History of Scientific Wikipedia Articles
🤖 AI Summary
This work addresses the challenge of tracking knowledge evolution in scientific Wikipedia articles, where substantive scientific edits are often obscured by numerous routine revisions. To this end, we propose WikiSTAR, an interactive system that combines an expert-designed multi-label taxonomy of scientific edits with a large language model–based classifier to assign fine-grained semantic annotations to revisions. WikiSTAR enables exploration across multiple analytical scales—from macro-level trends to individual edit instances—through hierarchical visualizations. Our system represents the first interactive, fine-grained approach to tracing the evolution of scientific content on Wikipedia, uncovering previously hidden patterns of knowledge change. User studies demonstrate that WikiSTAR effectively supports novel analytical tasks and advances research on scientific knowledge evolution. The system, source code, and a manually annotated benchmark dataset are publicly released.
📝 Abstract
Wikipedia plays a key role in shaping public understanding of science, and its openly accessible revision history is a unique record of how scientific knowledge evolves over time. Yet scientifically meaningful revisions are obscured by the sheer volume of routine edits, leaving each article's scientific history hidden. We present WikiSTAR (Scientific Tracking of Article Revisions), an interactive system for exploring scientifically meaningful changes across an article's revision history. Using an LLM classifier with an expert-designed multi-label taxonomy, WikiSTAR first tags edit types such as the addition of technical terms, new research findings, and changes in scientific narrative. Then, through interactive views, an article's full revision history can be traced at any granularity - from aggregate trends that reveal when and in which sections scientific content was added or refined, down to individual edits - showing how scientific knowledge develops at a scale previously impossible. In a user study, experts from three domains found that WikiSTAR surfaced new patterns and research questions and enabled previously impractical analyses. We release our system, code and a human-annotated benchmark.
Innovation
Methods, ideas, or system contributions that make the work stand out.
scientific revision tracking
LLM-based classification
interactive visualization
knowledge evolution
Wikipedia analysis
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