Short answer: yes—most modern websites can detect scraping. The real question is how they detect it and how to stay compliant and resilient when collecting publicly available data. Below is a practical overview, with example patterns you’ll also see in the code structure of this repo: Instagram Web Scraper
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How websites detect scraping
Server-side signals
Unnatural request rates (too many requests per second/minute from the same IP).
IP reputation & ASN patterns (datacenter IPs, known proxy ranges).
Missing/odd headers (incomplete Accept-Language, Accept, or cookies).
Repeated URL sequences (non-human crawl paths).
Failed JS challenges (can’t execute bot checks like token generation).
Login & session anomalies (many sessions from one IP/device).
Client-side signals
Headless/browser fingerprinting (WebDriver flags, canvas/audio anomalies).
Blocked resources (anti-bot scripts not executed properly).
Timing footprints (robotic click/scroll intervals, zero think-time).
For concrete code scaffolding and patterns, review the repo’s structure and notes:
Example usage & constraints in the README: Instagram Web Scraper docs
Is detection always bad?
Not necessarily. Some sites throttle or challenge first. Others block or soft-ban temporarily. Your goal is to reduce false flags, respect platform rules, and prefer official APIs where available. If you’re exploring patterns specific to Instagram-like pages, see the implementation references in this open source project: open-source Instagram web scraper
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Best practices to reduce flags (ethical & compliant)
Respect Terms & robots.txt: Only scrape what you’re allowed to.
Human-like pacing: Add randomized delays and backoff on errors.
Use proper headers: Send realistic Accept/Language headers; handle cookies.
Session management: Maintain and refresh sessions responsibly; avoid mass parallel logins.
Handle JS: Use a real browser where needed and load critical resources.
Error-aware retries: Distinguish between 4xx, 5xx, and challenge pages.
Data minimization: Only collect the fields you truly need.
Audit & logging: Keep transparent logs for compliance reviews.
You’ll find several of these ideas reflected in code organization and comments in the repo:
Check the code: Instagram-Automations/Instagram-web-scraper
Legal & ethical notes
Follow local laws and platform policies.
Do not bypass access controls or scrape private data.
Prefer official APIs when they provide the fields you need.
TL;DR
Web scraping can be detected through rate patterns, fingerprints, and session/JS checks. Design scrapers with compliance, realism, and restraint in mind. For a compact, practical starting point tailored to Instagram-style pages, explore the code and examples here: Instagram Web Scraper (GitHub)
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Call to action: Want to see a lean implementation and adapt it to your workflow? Dive into the repo, review the README, and clone the project: instagram web scraper
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