Порівняння методів

	Збір даних на основі API	Веб-скрейпінг
Галузь	Методологія опитувань	Методологія опитувань
Родина	Process / pipeline	Process / pipeline
Рік появи	2000s–2010s (formalized as a research method)	Late 1990s–2000s
Автор методу	Emerged from computational social science and web 2.0 platform practices	Early internet practitioners; systematised in research contexts from the late 1990s onward
Тип	Digital data collection technique	Automated digital data collection technique
Основоположне джерело	Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648	Mitchell, R. (2018). Web Scraping with Python: Collecting More Data from the Modern Web (2nd ed.). O'Reilly Media. ISBN: 978-1491985571
Інші назви	API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection	web harvesting, screen scraping, web crawling, automated data extraction
Пов'язані	5	5
Підсумок	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.	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.