Сравнение методов

	Веб-скрейпинг	Сбор данных через API
Область	Методология опросов	Методология опросов
Семейство	Process / pipeline	Process / pipeline
Год появления	Late 1990s–2000s	2000s–2010s (formalized as a research method)
Автор метода	Early internet practitioners; systematised in research contexts from the late 1990s onward	Emerged from computational social science and web 2.0 platform practices
Тип	Automated digital data collection technique	Digital data collection technique
Основополагающий источник	Mitchell, R. (2018). Web Scraping with Python: Collecting More Data from the Modern Web (2nd ed.). O'Reilly Media. ISBN: 978-1491985571	Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648
Другие названия	web harvesting, screen scraping, web crawling, automated data extraction	API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection
Связанные	5	5
Сводка	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.	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.