方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 多源API数据收集× | API数据收集× | |
|---|---|---|
| 领域 | 调查方法论 | 调查方法论 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2010s (accelerated with proliferation of public APIs) | 2000s–2010s (formalized as a research method) |
| 提出者≠ | Emergent practice in computational social science; formalized by Salganik, Ruths, Pfeffer, and others | Emerged from computational social science and web 2.0 platform practices |
| 类型≠ | Quantitative / mixed data collection technique | Digital data collection technique |
| 开创性文献≠ | Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213), 1063–1064. DOI ↗ | Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648 |
| 别名 | multi-API data harvesting, multi-platform API collection, cross-API data aggregation, federated API data collection | API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection |
| 相关≠ | 4 | 5 |
| 摘要≠ | Multi-source API-based data collection is a systematic technique in which a researcher simultaneously or sequentially queries two or more application programming interfaces (APIs) to harvest digital data for a research project. By drawing from multiple platforms or services — such as social media APIs, government open-data portals, or scientific data repositories — researchers can build richer, more representative datasets than any single source permits. The method is especially prominent in computational social science, digital humanities, public health surveillance, and environmental monitoring. | API-based data collection is a systematic technique in which a researcher sends structured requests to an application programming interface to retrieve data automatically from digital platforms, databases, or services. It is the primary method used in computational social science to gather large-scale social media records, government open data, financial data streams, and scientific repository content in machine-readable formats such as JSON or XML, enabling reproducible and scalable data acquisition that manual collection cannot match. |
| ScholarGate数据集 ↗ |
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