As web scraping applications continue to expand, the sources of data collected by businesses are also evolving. In the past, search engines, e-commerce platforms, and social media were the primary targets for web data collection. By 2026, AI platforms have emerged as a new category of high-demand data sources.
According to publicly available industry data, the most popular scraping targets in 2026 span search, short-form video, e-commerce, AI, developer platforms, and automation tools. TikTok continues to see high levels of scraping activity, while ChatGPT and Perplexity have entered the list of popular websites for the first time.
So, which websites are scraped most frequently in 2026? And what value does the data from each platform offer?
I. What Are the Most Scraped Websites in 2026?
According to publicly available web scraping industry data for 2026, the most popular data collection targets include social media platforms, AI platforms, and search engines. However, it is important to note that these rankings are typically based on scraping request statistics from specific datasets or platforms and do not represent an absolute ranking of all crawler requests across the internet. They are therefore better viewed as a reference for understanding web data collection trends in 2026.
Looking at how the rankings have changed, the most important trend is not whether a particular website has moved up or down, but that data collection targets are expanding from traditional websites to AI and technology platforms.
Popular Data Collection Websites in 2026
TikTok has held the top spot for the second consecutive year, with nearly one in every five scraping requests directed at the platform. ChatGPT and Perplexity have entered the top ten for the first time—signaling a shift in enterprise data collection from “tracking search results” to “monitoring AI-generated answers.”
II. Why Have These Websites Become Popular Data Sources?
The reasons teams collect data vary widely across industries, but they can generally be grouped into the following five categories.
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- Search Data**
Typical targets: Google / Bing / Naver
Search data has long been the foundation of the web scraping ecosystem. In 2026, the key change is that extracting data from Google search results is no longer about a flat list of links—it now needs to cover traditional search results, AI Overviews, AI Mode conversations, shopping modules, local results, and other response types. SEO teams need to track both traditional rankings and the sources cited in AI Overviews to fully understand a brand’s visibility across the search ecosystem.
After Microsoft shut down its official Search API in August 2025, demand for Bing SERP scraping increased significantly, making third-party scraping tools a major alternative.
2. E-commerce Data
Typical targets: Amazon / Walmart / Target / eBay
The core goals of e-commerce data collection are price monitoring, inventory tracking, and competitor analysis. According to industry data, retail and e-commerce account for 36.7% of all web scraping activity, making them the largest industry segment.
Although Amazon dropped from 3rd to 8th place in the 2026 rankings, it remains one of the most important data sources in e-commerce. Its anti-bot systems were further strengthened in 2026, limiting effective data collection to 20–50 products per session. Teams therefore need to carefully design request pacing and IP rotation strategies. Target’s first appearance on the list also reflects retailers’ growing need for product data in AI-driven shopping environments.
3. Social Media Data
Typical targets: TikTok / Instagram / Reddit / X
TikTok’s continued position at the top of the rankings is closely tied to its 18% share of scraping activity. Businesses and developers collect video metadata, user engagement data (views, likes, comments, and shares), comment content, and even TikTok Shop product information for trend analysis, content strategy, and social commerce performance evaluation.
Instagram and Reddit are also popular social media data sources. Community discussions on Reddit are widely used for consumer insights and sentiment analysis, while Instagram is a key platform for monitoring branded visual content.
4. AI Data (GEO)
Typical targets: ChatGPT / Perplexity
This is one of the biggest developments in 2026. ChatGPT and Perplexity have entered the top five most-scraped websites for the first time, giving rise to a new field—Generative Engine Optimization (GEO).
Brands now need to monitor: Does ChatGPT mention their brand in its answers? How often are competitors recommended? Which source pages does the AI cite? These insights directly influence a brand’s AI visibility strategy. Because Perplexity directly cites sources in its answers, it has become a specific target for citation tracking.
ChatGPT reached 900 million weekly active users in February 2026. Although Perplexity has a smaller user base, its answer format makes it a unique scraping target.
5. B2B Data
Typical target: LinkedIn
LinkedIn is a core platform for B2B data collection. Sales and operations teams collect structured data such as job information, company size, industry classification, and headquarters location to build prospect lists.
By 2026, LinkedIn data collection had evolved from simple HTML parsing to solutions that combine browser automation tools such as Playwright with proxy IP pools. Teams can filter by job title, company, industry, location, and other dimensions to identify target prospects more precisely.
III. What Challenges Does Web Data Collection Face, and How Can They Be Solved?
The real challenge in web data collection is often not retrieving a page or two, but maintaining a stable, highly available collection pipeline in complex scenarios involving high concurrency, multiple regions, and long-running tasks.
Below are the 6 most common core challenges in data collection engineering and their corresponding solutions:
1. IP Restrictions and Risk-Control Blocks
Target websites identify and block crawler traffic using IP reputation scores, ASN analysis, and request frequency. Data center IPs are particularly likely to be flagged—anti-bot systems on platforms such as Amazon can determine the nature of a source within a very short time after a request arrives. When a large number of requests come from the same IP, the target website’s risk-control system can quickly classify the activity as abnormal and trigger a block.
Solution: Using residential Proxy is currently one of the most effective approaches. Residential IPs are assigned by real ISPs to household users, making them harder for target websites to distinguish from normal user traffic. For example, IPFoxy’s residential Proxy service supports selecting target countries/regions and IP rotation modes (rotating or sticky), which can be configured in automated scraping scripts. Combined with reasonable concurrency controls, this can significantly reduce the risk of being blocked.
Proxy can help in this scenario in the following ways:
- IP Rotation: Send requests through different IPs to reduce the risk of a large volume of requests being concentrated on a single IP.
- Geo-Targeting: For websites such as Google, TikTok, and e-commerce platforms where content varies by region, choose IPs from the target market.
- Session Control: When you need to maintain a login session or continuous access, use sticky sessions. When frequent switching is required, rotate the IP on every request to adapt to different use cases.
- Concurrent Scraping: When collecting multiple keywords, pages, or data sources simultaneously, a well-managed IP pool can help distribute requests.
2. Request Rate and Concurrency Control
Nearly all major websites impose request-rate thresholds. Public Shopify pages typically allow around 2–4 requests per second, while the Twitter API further tightened its request intervals in 2026. Exceeding these limits may result in a 429 status code or, in more serious cases, a temporary or permanent IP ban.
Solution: Setting appropriate request intervals is the most basic approach. Add random jitter to scraping scripts, such as a random delay of 0.5–2 seconds, to mimic natural user behavior. More importantly, combine this with Proxy rotation to distribute requests across a large pool of IPs, keeping the request rate for each individual IP within a safe threshold.
3. Regional Content Differences and Personalization
Platforms such as Google, TikTok, and Amazon return different content and search results based on the visitor’s location. Teams working on cross-border e-commerce or global SEO need to collect accurate data from specific regions.
Solution: Use geo-targeted Proxy to route requests through the target country or city. For example, IPFoxy dynamic Proxy supports city-level targeting, helping ensure that the collected data matches what real users in the target market see.
4. CAPTCHAs and Anti-Bot Detection
Anti-bot systems such as Cloudflare, DataDome, and PerimeterX may present CAPTCHAs or JavaScript challenges when abnormal traffic is detected. These systems analyze not only IPs, but also browser fingerprints, TLS fingerprints, Canvas rendering characteristics, and other signals.
Solution: Proxy alone is not enough to fully bypass advanced anti-bot systems. A recommended approach is to combine Proxy rotation with browser fingerprint masking tools. Specifically, plugins such as puppeteer-extra-plugin-stealth can reduce automation signals, while high-anonymity Proxy can be used to rotate requests. This is a proven combination for many scraping scenarios.
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- Managing High-Concurrency Requests**
As collection volume grows from hundreds of pages to hundreds of thousands or even millions, concurrency management becomes a core challenge. Conventional optimization techniques such as connection-pool reuse and KeepAlive connections can instead cause IP rotation to fail when Proxy IPs are frequently switched.
Solution: Use a proxy tunneling architecture for dynamic IP rotation, disable KeepAlive, and dynamically clear the connection pool to ensure that each request is sent through a new IP. IPFoxy residential Proxy offers an unlimited plan billed by duration, with customizable bandwidth and no traffic cap, making it particularly suitable for large-scale data collection tasks.
IV. FAQ
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- What Is Web Scraping? ** Web Scraping is the technique of automatically extracting publicly available data from websites using software. It is commonly used for market research, price monitoring, search ranking analysis, competitor research, and other applications.
2. Why Are ChatGPT and Perplexity Becoming Data Collection Targets?
Businesses are increasingly interested in how AI platforms answer questions about their brands, products, and industries. As a result, they analyze AI responses, brand mentions, and cited sources—one of the reasons GEO is gaining attention.
3. Which Proxy Is Best for Web Scraping?
The right choice depends on the target website and the scraping scenario. If you need to simulate access from a real user’s region, residential Proxy is usually a better fit. If a fixed IP is more important, static ISP Proxy can be considered. For large-scale tasks where cost and concurrency are key considerations, data center Proxy may be suitable depending on the target website.
V. Conclusion
The most popular data collection targets in 2026 show a clear trend toward diversification. TikTok represents social media and short-form video data, Google represents search data, Amazon and Target represent e-commerce data, while ChatGPT and Perplexity represent the rapidly growing field of AI data collection.
This means Web Scraping is no longer just the traditional concept of “web crawling.” For businesses, it is becoming essential infrastructure for market research, price monitoring, SEO, GEO, competitor analysis, and data-driven decision-making.
For large-scale, cross-regional data collection, writing a stable scraping program is only part of the equation. Teams also need to consider IP quality, rotation strategies, and request concurrency. Choosing the right Proxy type can make the entire data collection process more stable and better suited for long-term operation.
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