Web Scraping Bots

Legal considerations for web scraping:

Generally legal:

(1) Scraping publicly accessible data (no login required)

(2) Complying with robots.txt and website terms of service

(3) Using scraped data for research, analysis, or B2B prospecting

(4) Respecting rate limits to avoid overwhelming servers

Legally risky:

(1) Scraping behind login walls or paywalls

(2) Violating website Terms of Service (though enforceability varies)

(3) Bypassing technical protection measures (CAPTCHAs, IP blocks)

(4) Re-publishing scraped content as your own

(5) Violating GDPR or privacy laws with personal data

Notable cases:

(1) LinkedIn vs hiQ (2022): Court ruled scraping public data is generally legal

(2) Platform-specific: LinkedIn, Facebook, Twitter actively fight scrapers despite court rulings

Best practices:

(1) Only scrape public data, not user-generated private content

(2) Use rate limiting (don't hammer servers)

(3) Respect robots.txt when possible

(4) Be aware some platforms (LinkedIn, Instagram) will block aggressive scrapers

(5) Use scraped data responsibly, comply with GDPR for EU contacts

Bottom line: Public B2B data scraping for prospecting is widely practiced and generally defensible, but understand platform-specific risks.

Top scraping platforms by use case and technical level:

No-code scraping (non-technical users):

(1) Phantombuster: Pre-built scrapers for LinkedIn, Twitter, Instagram, Google Maps. Best for common use cases.

(2) Apify: Marketplace of 1,500+ pre-built scrapers + cloud infrastructure. Great for scaling.

(3) Octoparse: Point-and-click visual scraper builder. Good for custom website scraping.

(4) Bardeen: Browser automation for simple scraping tasks, integrates with workflows

Low-code scraping (some technical knowledge):

(1) Bright Data: Enterprise web scraping with proxy network and API. Best for large-scale operations.

(2) ScrapingBee: API-based scraping with headless browser support

(3) ParseHub: Visual scraper with JavaScript rendering and pagination handling

Developer tools (code required):

(1) Scrapy (Python): Open-source framework for building custom scrapers

(2) Puppeteer (Node.js): Headless Chrome automation for JavaScript-heavy sites

(3) Beautiful Soup (Python): HTML parsing library for simple scraping

LinkedIn-specific:

(1) Phantombuster LinkedIn scrapers (most popular)

(2) Waalaxy (combines scraping + outreach)

(3) Expandi (cloud-based LinkedIn automation)

Best practice: Start with Phantombuster for LinkedIn/social scraping, use Apify for general web scraping, graduate to Bright Data for enterprise-scale needs.

Anti-detection techniques used by modern scrapers:

IP rotation and proxies:

(1) Residential proxies: Rotate through real home IP addresses (harder to detect)

(2) Datacenter proxies: Cheaper but easier to block

(3) IP rotation: Change IP after every N requests to avoid rate limits

(4) Geographic distribution: Use IPs from target country to avoid geo-blocks

Browser fingerprinting evasion:

(1) Headless browser detection: Simulate real Chrome/Firefox, not headless mode

(2) User-agent rotation: Vary browser signatures

(3) JavaScript execution: Render pages like real browser, not just fetch HTML

(4) Cookie and session handling: Maintain realistic browsing sessions

Behavioral anti-detection:

(1) Human-like timing: Random delays between requests (2-10 seconds)

(2) Mouse movement simulation: Mimic human scrolling and clicking

(3) Page interaction: Click buttons, scroll, interact before scraping

(4) Respect rate limits: Don't hammer servers with hundreds of requests per second

CAPTCHA solving:

(1) CAPTCHA-solving services: 2Captcha, Anti-Captcha (pay per solve)

(2) reCAPTCHA bypass: Use residential proxies + browser fingerprinting

(3) hCaptcha solutions: AI-powered image recognition

Cloud-based scraping:

(1) Distributed scraping: Run bots from multiple servers/locations

(2) Managed infrastructure: Let platform handle anti-detection (Bright Data, Apify)

Success rates:

(1) Simple sites: 95%+ success with basic proxies

(2) LinkedIn, Facebook, Instagram: 60-80% success with premium tools

(3) Heavily protected sites: May need human-supervised scraping

Best approach: Use reputable platform (Phantombuster, Apify) that handles anti-detection automatically vs building custom solution.

Pricing for web scraping platforms:

No-code platforms (per month):

(1) Phantombuster: $30-69/month for 10-40 hours of automation runtime

(2) Apify: Pay-per-use, typically $50-200/month for moderate scraping

(3) Octoparse: $75-209/month for cloud-based scraping

(4) Bardeen: Free for individuals, $10-20/month for premium features

Enterprise scraping:

(1) Bright Data: Starting $500/month, scales to $5,000+ for large operations

(2) ScrapingBee: $49-449/month based on API calls

(3) ParseHub: $149-499/month for high-volume scraping

Proxy costs (required for scale):

(1) Residential proxies: $5-15 per GB of bandwidth

(2) Datacenter proxies: $1-3 per GB (cheaper but more likely to be blocked)

(3) CAPTCHA solving: $1-3 per 1,000 CAPTCHAs solved

Developer tools (self-hosted):

(1) Scrapy, Beautiful Soup: Free (open source)

(2) Server costs: $10-100/month for VPS hosting

(3) Proxy costs: $100-500/month for reliable proxies

Pricing factors:

(1) Volume: How many pages/profiles to scrape

(2) Complexity: JavaScript rendering and anti-detection increase cost

(3) Speed: Faster scraping requires more proxies and infrastructure

ROI calculation:

(1) Manual data entry: $15-25/hour for VA or intern

(2) Scraping 1,000 leads manually: 10-15 hours = $150-375

(3) Automated scraping: Same 1,000 leads = $5-20 in platform costs

Break-even: If you need >100 leads/month, automated scraping pays for itself immediately.