What is AI scraping?

AI allows sophisticated models to scrape websites more intelligently, adjusting to changing digital environments with adaptive workflows and even performing in a more ethical fashion, avoiding the pitfalls of simpler web scraping tools.

AI scraping also makes the process easier and more cost-effective. Now e-commerce startups can use it to perform market research and social media analytics. Academic researchers can analyze news articles, Amazon product listings or job postings on LinkedIn. SEO specialists can monitor keyword rankings, backlinks and competitor websites to stay ahead. And AI companies can use extracted data to train their models.

No-code interfaces such as browse.ai  make it simple to build scrapers using templates, drag-and-drop actions and automated triggers. These AI tools can even capture screenshots of each page for auditing.

While not inherently illegal or unethical, web scraping can be problematic when performed in an unethical manner. Legitimate use cases include data analysis, research and competitive price monitoring. Scraping tasks commonly considered to be unethical include the scraping of private data, overloading servers or plagiarizing content. It is the responsibility of the practitioner to gather data ethically, and in compliance with relevant regulatory frameworks on data collection.

Traditional web scraping and AI-powered web scraping pursue the same goal, but differ in how they interpret web pages. Traditional web scraping is typically done with manually coded scripts in the form of CSS selectors, XPath expressions and hard-coded logic. These rules enable the scraper to navigate pages and locate specific elements on the page. They perform well when the structure is stable.

Traditional web scrapers send an HTTP request to a web server, and the server responds with the page content in HTML. After fetching the HTML, the scraper interprets the data with tools like BeautifulSoup, lxml or Cheerio to create a parse tree, representing the hierarchical structure of the page: the document object model, or DOM.

Scrapers use expressions, written pattern or rules used to locate a specific piece of web data inside an HTML document. These include selectors, specific instructions that tell the scraper which parts of the webpage to retrieve, regex rules, pattern-matching formula used to identify raw text, and logic rules, custom rules written in code to decide how and what to extract.

Once elements are located, text is extracted, attributes collected, and data cleaned to remove irrelevant information and enforce formatting consistency. This newly structured data is stored in a preferred file format such as an Excel spreadsheet, Google Sheet or CSV file.

Since scrapers rely on fixed rules, minor website changes can break the scraper, with different website requiring unique logic to be successfully scraped. Traditional methods struggle when dealing with dynamic pages with unstructured and multimodal content.

There are a number of ways that machine learning can be used to automate large-scale scraping.

• Unstructured data collection
   

• Handling complex web environments
   

• Reduced maintenance
   

• Semantic understanding
   

• Improved data quality
   

• Resilience and efficiency

AI broadens the scope of what can be scraped. It can handle different languages and analyze data in the form of images, video, diagrams and PDFs. Instead of just extracting the text on the page, AI transforms raw multimodal information into structured data. It gets closer to the nuance and understanding that a human researcher could provide.

The rigid logic of traditional rules-based scraping was better suited to a static web. But many websites no longer serve simple, static HTML. The web is a living thing, and website architectures are constantly evolving to allow for JavaScript and other dynamic content elements such as infinite scrolling and widgets that update as the user experiences the page. As a result, traditional scrapers often break or deliver unwanted results when exposed to a new class name or container. What’s more, many websites deploy tools to intentionally thwart automated AI agent scrapers—even ones operating ethically.

AI models possessing semantic understanding gained from their training on large corpora of data improves the scraper’s ability to handle these complex, changing environments. AI can infer where meaningful content resides even when structural cues are inconsistent or hidden.

Traditional scrapers need updates to keep up with a changing web. AI-powered scrapers, however, generalize and adapt across different designs and layouts. Even when websites are redesigned, a large language model (LLM) can identify, for example, that a specific number is a price, or that a name is an author, because it recognizes deeper patterns of language and how entities are presented on the page.

Website real estate is messy, with different types of navigational elements, boilerplate at the bottom of the page, dynamic sidebars and other distractions. Scrapers equipped with natural language processing (NLP) can filter out content that isn’t useful and focus on meaningful information on each page.

It can be said that where traditional scrapers gather data, AI scrapers gather knowledge. They identify entities and understand the relationships between them. They can perform sentiment analysis or categorize content by topic.

The result of all this deeper understanding is the transformation of raw, disorderly content into clean, consistent datasets. This makes for better downstream analysis. This functionality is especially valuable in specialized industries like finance or healthcare, where context is often more important than simply capturing text.

Where a traditional scraper might get blocked by a captcha, rate limiting or other throttling methods, smarter models can choose from among different strategies to avoid triggering anti-bot measures in order to access content. Payment sites, subscription services and other secure pages might require a credit card or other type of authentication. AI scrapers can navigate these pages safely and compliantly when permitted by the site’s terms.

AI can direct a scraper to crawl at a certain time of day, at a specific rate and only among pages which are likely to yield useful scraped data. These measures make AI web scrapers more efficient to operate.

The modern revolution in AI would not have been possible without web scraping, and fortunately, AI can in turn help make the practice of scraping more ethical.

One of the primary issues in traditional scraping is overloading servers with repeated requests. Traditional scrapers operate indiscriminately, hitting websites thousands of times per hour. Websites will often use a number of techniques to block scrapers for this reason alone. AI can help by intelligently scheduling requests and adapting scraping speed based on server response times. Models can detect server stress patterns and throttle their own requests to avoid disrupting a website’s operations. Models can avoid crawling pages that are unlikely to yield useful information, which minimizes costs imposed on those website owners.

Some website administrators simply don’t want scrapers crawling their websites for whatever reason, and use signals like robots.txt files, API guidelines or other published usage restrictions. Scrapers can intelligently conform to these rules, choosing legal API endpoints and voluntarily avoiding prohibited content.

Another concern is privacy. Simple web scraping approaches can hoover up sensitive or personally identifiable information (PII) such as email addresses, phone numbers or other contact info. AI can filter out or anonymize sensitive data in real-time as it is ingested.

AI scrapers can also log their decisions for explainability and provide audit trails. This transparency helps organizations verify their processes and adhere to ethical standards, especially when using scraped data to train AI models.

To avoid plagiarism and respect content creators, models can be selective about what data they ingest. For example, instead of scraping entire pages or websites, they can instead summarize content, aggregate statistics and use embeddings rather than verbatim text.

Smarter scraping approaches can also avoid data that could perpetuate bias, harassment or misinformation when that data is intended for use in model training.