DataParasite Enables Scalable and Repurposable Online Data Curation
This provides a scalable and reusable solution for researchers in computational social science and related fields, though it is incremental as it builds on existing agentic search and extraction methods.
The paper tackles the problem of labor-intensive and costly online data curation in computational social science by introducing DataParasite, an open-source pipeline that reduces data-collection costs by an order of magnitude while achieving high accuracy across tasks like faculty hiring histories and political career trajectories.
Many questions in computational social science rely on datasets assembled from heterogeneous online sources, a process that is often labor-intensive, costly, and difficult to reproduce. Recent advances in large language models enable agentic search and structured extraction from the web, but existing systems are frequently opaque, inflexible, or poorly suited to scientific data curation. Here we introduce DataParasite, an open-source, modular pipeline for scalable online data collection. DataParasite decomposes tabular curation tasks into independent, entity-level searches defined through lightweight configuration files and executed through a shared, task-agnostic python script. Crucially, the same pipeline can be repurposed to new tasks, including those without predefined entity lists, using only natural-language instructions. We evaluate the pipeline on multiple canonical tasks in computational social science, including faculty hiring histories, elite death events, and political career trajectories. Across tasks, DataParasite achieves high accuracy while reducing data-collection costs by an order of magnitude relative to manual curation. By lowering the technical and labor barriers to online data assembly, DataParasite provides a practical foundation for scalable, transparent, and reusable data curation in computational social science and beyond.