This paper presents a comprehensive review of web scraping and its growing role in modern data-driven systems. Web scraping, the automated extraction of structured data from websites, has evolved into a critical technology supporting search engines, financial intelligence platforms, biomedical research, tourism analytics, marketing systems, and social media analysis. The study synthesizes findings from multiple peer-reviewed research articles published between 2013 and 2023 and examines the conceptual foundations, methodologies, tools, applications, challenges, and future directions of web scraping technologies. The paper compares traditional extraction methods such as regular expressions and HTML parsing with advanced machine-learning and computer-vision-based scraping approaches. It also evaluates widely used frameworks and libraries including BeautifulSoup, Scrapy, and Selenium. In addition, the review explores real-world applications across tourism intelligence, healthcare, bioinformatics, academic research, and social media analytics. Particular attention is given to anti-scraping mechanisms, ethical and legal considerations, and the integration of Natural Language Processing (NLP) techniques for converting unstructured web data into actionable insights. The paper further discusses evolving adversarial dynamics between web scrapers and anti-bot defenses, platform vulnerabilities, and emerging AI-assisted scraping systems. The study concludes with a forward-looking discussion on regulatory developments, responsible data extraction practices, and future research opportunities in AI-augmented web intelligence systems.
Sharma et al. (Sat,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: