Сравнение методов

	Лонгитюдный веб-скрейпинг	Веб-скрейпинг
Область	Методология опросов	Методология опросов
Семейство	Process / pipeline	Process / pipeline
Год появления	2000s–2010s	Late 1990s–2000s
Автор метода	Emergent practice in computational social science; formalized across internet research community	Early internet practitioners; systematised in research contexts from the late 1990s onward
Тип	Automated longitudinal data collection	Automated digital data collection technique
Основополагающий источник	Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 978-0691158648	Mitchell, R. (2018). Web Scraping with Python: Collecting More Data from the Modern Web (2nd ed.). O'Reilly Media. ISBN: 978-1491985571
Другие названия	repeated web scraping, time-series web data collection, longitudinal crawling, panel web scraping	web harvesting, screen scraping, web crawling, automated data extraction
Связанные	5	5
Сводка	Longitudinal web scraping is a data collection technique that uses automated scripts to extract content from websites at multiple, predefined time points. By revisiting the same web sources repeatedly, researchers build a time-series dataset that captures how online content, prices, discourse, or behavior evolves. It is widely used in computational social science, economics, political science, health research, and digital humanities to study change without relying on retrospective self-report.	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.