DataParasite Enables Scalable and Repurposable Online Data Curation

2026-01-05

arXiv.org

AI Summary

This work proposes an open-source, modular data collection pipeline to address the labor-intensive, costly, and poorly reproducible nature of dataset construction from heterogeneous online sources in computational social science. The system employs lightweight natural language instructions to configure workflows, decomposing tabular data curation into entity-level search and structured extraction tasks. By leveraging large language model–based intelligent agents, it achieves a task-agnostic, reusable architecture that operates effectively even without predefined entity lists. Evaluated across multiple representative tasks, the approach attains high accuracy while reducing data acquisition costs by an order of magnitude compared to manual methods, substantially lowering both technical and human resource barriers to entry.

Technology Category

• Humans and AI: Crowd Sourcing and Human Computation
• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Information Extraction

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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.

Problem

• computational social science
• online data curation
• data reproducibility
• heterogeneous data sources
• scalable data collection

Innovation

• DataParasite
• modular pipeline
• agentic search
• natural-language instructions
• scalable data curation