Dealing With Anti-Scraping Measures In Public Data Gathering

Businesses seeking a competitive edge over rivals need data to drive effective decision-making. One of the major differences between primary and secondary data is that the latter does not require direct contact with the subjects under research, while the former does.

Furthermore, primary data is obtainable via surveys and company-sponsored research. On the other hand, secondary data is available from previously conducted research online or from public sources. The public data of interest to this article is secondary data. Businesses can collect the data manually or automatically with the help of web scrapers.

However, such web-scraper-mediated public data collection faces some challenges. For companies to collect this data optimally and efficiently, they must prepare to tackle these challenges. Read on to learn about anti-scraping measures, why they are so challenging, and how web scrapers tackle this challenge.

Why do websites use anti-scraping measures

Other than privacy and data security, one such reason for blocking the scraping of public data is website performance. Some of the others are:

• To maintain their competitive advantages over rival businesses and products;
• To ensure regulatory compliance with the relevant data protection laws in the region where they operate;
• To ensure equitable distribution of server performance among all users of a website;
• To prevent misuse of resources present on the website by malicious entities.

Common anti-scraping measures

Companies need data. Where the data they need is available in bulk online, businesses may use web scrapers. Though the web scrapers are efficient for the companies, they could create efficiency issues for the websites. For instance, lots of requests from the scraping bots can overload a web server and potentially reduce load times significantly.

The consequence is that website administrators attempt to reduce server load and increase load times by impeding the activities of scrapers. Some of the measures they employ to achieve this are:

Captchas: Captchas are challenge-response prompts that websites use to determine if a visitor is a human or a bot. The challenge could involve users picking relevant images off a grid or solving a puzzle. Captchas are arguably the most common anti-scraping measures, but some of the best web unblockers and scrapers have the capabilities to beat this measure.

Rate Limiting: With rate limiting, websites can significantly slow the activity of scraping bots rather than precluding them entirely. The websites look out for unusual user behavior, such as numerous requests originating from a certain IP address within a short period. This way, site administrators can reduce scraping and maintain performance without affecting the browsing speeds of other users. The only way to avoid the rate-limiting mechanism is to spread scraping activity over a larger period or use rotating proxies to share them over multiple IP addresses.

JavaScript challenge: A website can utilize JavaScript to load content in a dynamic form. As such, they might appear in non-standard formats that dissuade users from utilizing scrapers on the site’s pages. Alternatively, some websites may add certain random elements to their data to disturb the structure that the web scraper expects. An Intelligently written scraping algorithm can help with both problems.

Honeypots: Honeypots are web page links that are visible only to scrapers. When an automated web scraper interacts with a website’s honeypot, it indicates unusual user behavior, which can lead to an IP ban.

Robots.txt: Similar to honeypots, robots.txt are intentionally placed files on a webpage. They direct a scraper on what content its administrators permit it to scrape and what not to scrape.

User behavior analysis: Such analysis involves monitoring user patterns on the web pages and identifying requests that consistently veer from such patterns. Some behavioral patterns monitored include time spent on pages, navigation patterns, etc. Behavioral-based defense mechanisms are the most difficult for web scrapers to beat.

All of these challenges might prove too hard to overcome for some web scrapers. As such, effective data collectors use reliable tools, such as Oxylabs’ Web Unblocker, to gain access to web pages where necessary.

Why are anti-scraping measures so challenging?

Some may point to the sophistication of an anti-scraping measure as the reason they prove so effective against web scrapers. However, more often than not, it is the automated nature of a web scraper that makes the measure so challenging for them to beat.

The absence of a human element guiding every step of the scraping process makes it susceptible to cleverly placed obstructions like honeypots, JavaScripts, randomized elements, etc. However, even the more challenging anti-scraping measures are beatable if the human element is more adept. For instance, a carefully written or configured algorithm can deal with changes in content structure, captchas, and honeypots.

Conclusion

For a website, maintenance of site performance and preservation of user experience are very important. Anti-web scraping measures help ensure the success of the website in this regard. On the other hand, a company looking to access publicly available data from such websites needs it regardless of the measures put in place.

The solution is to use well-designed web scrapers, usually in combination with a web unblocking solution. Logic holds that the web unblocker ensures access where it would otherwise be revoked, while the scraper can focus on extracting the data. Reliable web unblocking tools and web scrapers can ensure data gathering without any hiccups.