방법 비교

	다중 소스 API 기반 데이터 수집	웹 스크래핑
분야	조사방법론	조사방법론
계열	Process / pipeline	Process / pipeline
기원 연도	2010s (accelerated with proliferation of public APIs)	Late 1990s–2000s
창시자	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
유형	Quantitative / mixed data collection technique	Automated digital data collection technique
원전	Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213), 1063–1064.	Mitchell, R. (2018). Web Scraping with Python: Collecting More Data from the Modern Web (2nd ed.). O'Reilly Media. ISBN: 978-1491985571
별칭	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
관련	4	5
요약	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.