Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Сбор данных на основе множественных API× | Веб-скрейпинг× | |
|---|---|---|
| Область | Методология опросов | Методология опросов |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 2010s (accelerated with proliferation of public APIs) | Late 1990s–2000s |
| Автор метода≠ | Emergent practice in computational social science; formalized by Salganik, Ruths, Pfeffer, and others | Early internet practitioners; systematised in research contexts from the late 1990s onward |
| Тип≠ | Quantitative / mixed data collection technique | Automated digital data collection technique |
| Основополагающий источник≠ | Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213), 1063–1064. DOI ↗ | Mitchell, R. (2018). Web Scraping with Python: Collecting More Data from the Modern Web (2nd ed.). O'Reilly Media. ISBN: 978-1491985571 |
| Другие названия | multi-API data harvesting, multi-platform API collection, cross-API data aggregation, federated API data collection | web harvesting, screen scraping, web crawling, automated data extraction |
| Связанные≠ | 4 | 5 |
| Сводка≠ | 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. | Web scraping is a computational data collection technique in which software automatically retrieves and extracts structured or semi-structured content from websites. Widely used in social science, computational linguistics, economics, and information science, it enables researchers to assemble large datasets from publicly accessible web sources — such as news archives, social media platforms, government portals, and online marketplaces — that would be impractical to collect manually. |
| ScholarGateНабор данных ↗ |
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