webscraped

Webscraped is an adjective used to describe data or content that has been obtained through automated collection of information from the web, typically via web scraping. It often refers to datasets assembled from multiple sites or pages for analysis, research, or monitoring.

The typical workflow involves identifying target sites, respecting robots.txt and terms of service, making HTTP requests, and parsing the returned HTML, JSON, or other responses. Data is extracted using selectors or patterns, then cleaned, normalized, and stored in structured formats such as CSV, JSON, or databases. For sites that rely on dynamic content, headless browsers or rendering engines (such as Selenium or Playwright) may be used to render pages before extraction.

Common use cases include price monitoring and market research, aggregation of news or product details, extraction of reviews or metadata, and support for data mining or machine learning pipelines. Webscraped data can support benchmarking, sentiment analysis, and longitudinal studies, among other applications.

Legal, ethical, and compliance considerations are important. Webscraped data may be subject to copyright, terms of use, and privacy laws. Respect for robots.txt, rate limiting, and the potential impact on server resources should be considered. Legal permissibility and permissible uses vary by jurisdiction and site policy, and some sites explicitly prohibit automated harvesting.

Quality and reliability concerns include source stability, changes in site structure, and anti-scraping defenses. Data provenance, timestamps, deduplication, and validation are important to maintain usefulness. When possible, using official APIs or publicly licensed datasets is preferred as an alternative to broad scraping.

See also: web scraping, data extraction, data harvesting.