The article examines the theoretical and practical aspects of using web scraping for automated data collection in economics. Web scraping enables the extraction of large volumes of information from web resources, making it particularly valuable for price analysis, market trend monitoring, and evaluating competitive activity. The focus is on the application of modern tools such as Python and its libraries (BeautifulSoup, Scrapy, Selenium), as well as the use of analytical platforms, databases, and cloud solutions for data storage and processing. The article describes the key stages of the web scraping process, including source identification, data extraction, parsing, storage, and analysis. Special attention is paid to legal and ethical aspects, such as compliance with copyright laws and data confidentiality, along with recommendations for the lawful use of technology. Practical examples illustrate how web scraping is applied to monitor prices in the Russian market, analyze consumer reviews, and predict price changes. The article also explores the prospects for web scraping development, including integration with artificial intelligence and machine learning, positioning it as a vital tool in the digital transformation of the economy.
Nikulin et al. (Fri,) studied this question.