方法对比

	纵向网络爬取	API数据收集
领域	调查方法论	调查方法论
方法族	Process / pipeline	Process / pipeline
起源年份	2000s–2010s	2000s–2010s (formalized as a research method)
提出者	Emergent practice in computational social science; formalized across internet research community	Emerged from computational social science and web 2.0 platform practices
类型	Automated longitudinal data collection	Digital data collection technique
开创性文献	Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 978-0691158648	Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648
别名	repeated web scraping, time-series web data collection, longitudinal crawling, panel web scraping	API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection
相关	5	5
摘要	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.	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.