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General Questions

Social Analyzer is an API, CLI, and web application for analyzing and finding a person’s profile across 1000+ social media websites. It uses multiple detection techniques and a rating mechanism to identify profiles with varying levels of confidence (0-100%).The tool is designed for OSINT (Open Source Intelligence) investigations and can help identify profiles related to suspicious or malicious activities such as cyberbullying, cyber grooming, cyberstalking, and spreading misinformation.
Yes, Social Analyzer is open source and free to use under the AGPL-3.0 license. The source code is available on GitHub at https://github.com/qeeqbox/social-analyzer.Note that some law enforcement agencies use an enhanced detection database that differs from the public version.
Social Analyzer is available in:
  • Python 3 (CLI and module)
  • Node.js (CLI and web app)
You can use it as a command-line tool or import it as a module in your Python scripts.
Social Analyzer is licensed under AGPL-3.0, which requires that any derivative works or services using the code must also be open source. If you plan to use it commercially, review the license terms carefully or contact the author for alternative licensing arrangements.

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Detection and Accuracy

Social Analyzer uses a rating mechanism that produces confidence scores from 0 to 100%:
  • 0-33%: Low confidence (“No”)
  • 34-66%: Medium confidence (“Maybe”)
  • 67-100%: High confidence (“Yes”)
The detection system is designed to minimize false positives. Accuracy varies by website based on:
  • Site structure and consistency
  • Anti-bot protection measures
  • Profile privacy settings
  • Username availability and uniqueness
For best results, filter for “good” status profiles (high confidence matches).
Social Analyzer employs multiple detection techniques:
  1. Normal Detection: Standard HTTP requests and HTML parsing
  2. Advanced Detection: Selenium WebDriver for JavaScript-heavy sites
  3. Special Detection: Custom logic for specific platforms (Facebook, Gmail, Google)
  4. OCR Detection: Optical character recognition using Tesseract
You can select detection modes using the --mode flag:
  • fast: FindUserProfilesFast (HTTP only)
  • slow: FindUserProfilesSlow (includes WebDriver)
  • special: FindUserProfilesSpecial (platform-specific methods)
“Maybe” results (medium confidence) occur when:
  • The username exists but the profile is private or restricted
  • The page structure is ambiguous
  • Anti-bot protections interfere with detection
  • The username matches but additional verification is needed
To see only high-confidence results:
python3 -m social-analyzer --username "johndoe" --filter "good"
Manually verify “maybe” results by visiting the URLs directly.
Generally no. Social Analyzer detects active, publicly accessible profiles. Deleted or suspended accounts typically return “not found” pages, which are filtered out.However, you can:
  • Use Wayback Machine integration to find archived profiles
  • Check the failed results which might include recently deleted accounts
  • Use specialized forensic tools for deleted content recovery

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Usage and Features

Fast Mode (--mode fast):
  • Uses only HTTP requests (no browser automation)
  • Faster execution (seconds to minutes)
  • Lower resource usage
  • May miss JavaScript-rendered content
  • Best for quick scans of many users
Slow Mode (--mode slow):
  • Uses Selenium WebDriver (browser automation)
  • Slower execution (minutes to hours)
  • Higher accuracy for modern web apps
  • Captures JavaScript-rendered content
  • Best for thorough investigation of specific users
Separate usernames with commas (no spaces):
python3 -m social-analyzer --username "johndoe,janedoe,john_doe123"
This is useful for:
  • Correlating multiple aliases of the same person
  • Batch processing investigation lists
  • Finding connections between accounts
  • Testing username variations
Metadata extraction (--metadata flag) pulls additional information from detected profiles:
  • Profile descriptions and bios
  • Posted content snippets
  • Profile images and avatars
  • Account creation dates (when available)
  • Location information
  • Social connections (followers, following)
Usage:
python3 -m social-analyzer --username "johndoe" --metadata --extract
This feature uses the QeeqBox OSINT module for enhanced data collection.
Yes, use the --websites parameter:
# Single site
python3 -m social-analyzer --username "johndoe" --websites "github"

# Multiple sites (space-separated)
python3 -m social-analyzer --username "johndoe" --websites "github twitter instagram"
This significantly speeds up searches when you know which platforms to target.
Export results in JSON format:
python3 -m social-analyzer --username "johndoe" --output "json" > results.json
You can then:
  • Import into spreadsheets (Excel, Google Sheets)
  • Process with data analysis tools
  • Integrate with other OSINT tools
  • Store in databases
  • Generate reports
See the Integration Guide for more examples.
Yes, you can configure proxy settings in the code by modifying the headers and session configuration. For custom proxy usage:
from importlib import import_module

SocialAnalyzer = import_module("social-analyzer").SocialAnalyzer()
SocialAnalyzer.headers = {
    "User-Agent": "Your-Custom-User-Agent"
}
# Add proxy configuration to session
Proxies are useful for:
  • Avoiding rate limiting
  • Bypassing geo-restrictions
  • Distributing load
  • Maintaining anonymity

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Technical Questions

Minimum Requirements:
  • Python 3.6+ or Node.js 12+
  • 2 GB RAM
  • Internet connection
  • 500 MB disk space
Recommended:
  • Python 3.8+ or Node.js 14+
  • 4 GB RAM
  • SSD storage
  • Stable internet connection
Optional Components:
  • Firefox or Chrome (for screenshots and advanced detection)
  • Tesseract OCR (for OCR-based detection)
  • Docker (for containerized deployment)
Yes, Social Analyzer works in headless environments. However:
  • CLI mode: Works perfectly without GUI
  • Screenshots: Require a display or virtual display (Xvfb)
  • Advanced detection: May need headless browser configuration
For headless servers:
# Install virtual display
sudo apt-get install xvfb

# Run with virtual display
xvfb-run python3 -m social-analyzer --username "johndoe"
Alternatively, use Docker which handles headless configuration automatically.
Search time varies significantly based on:Search Scope:
  • All 1000+ sites: 10-30 minutes
  • Top 100 sites: 2-5 minutes
  • Specific sites: 10-60 seconds
Detection Mode:
  • Fast mode: 50-200ms per site
  • Slow mode: 2-10 seconds per site
Additional Features:
  • Metadata extraction: +30-50%
  • Screenshots: +100-200%
Optimization Tips:
  • Use --top to limit site count
  • Use --websites for specific targets
  • Increase worker count (code modification)
  • Use Docker grid mode for parallel processing
Social Analyzer itself does not maintain a persistent database of searches or results. However:Data Storage:
  • Logs are saved to temp directory if --logs flag is used
  • Screenshots are saved locally if --screenshots flag is used
  • JSON output is saved only if you redirect it to a file
Privacy Notes:
  • No data is sent to external servers (except target websites)
  • All processing is local
  • You control all output and storage
  • Clean up logs and screenshots after use
The tool is designed for local use, not as a service.
Yes, you can modify the data/sites.json file to add new websites. Each entry requires:
{
  "url": "example.com",
  "selected": "false",
  "detections": [
    {
      "return": "true",
      "check": "username in page"
    }
  ]
}
However:
  • Manual testing is required for each site
  • Detection logic must be customized per site
  • Anti-bot measures may affect success
  • Consider contributing back to the main project
The --method parameter controls result filtering:find: Returns only detected profiles (positive matches)
python3 -m social-analyzer --username "johndoe" --method "find"
get: Returns all profiles regardless of detection status
python3 -m social-analyzer --username "johndoe" --method "get"
all: Combines both approaches (default behavior)
python3 -m social-analyzer --username "johndoe" --method "all"
Most users should use “find” or “all” for investigation purposes.
When using Social Analyzer, consider:Ethical Guidelines:
  1. Only investigate with legitimate purpose
  2. Respect privacy and personal boundaries
  3. Handle discovered information responsibly
  4. Don’t use for harassment or intimidation
  5. Secure any collected data appropriately
  6. Follow your organization’s ethics policies
Best Practices:
  • Document your investigation purpose
  • Obtain proper authorization when required
  • Minimize data collection to what’s necessary
  • Delete data when no longer needed
  • Protect subject privacy in reports
Social Analyzer can be used as part of background research, but:Important Limitations:
  • Cannot verify identity with certainty
  • May find profiles of different people with same username
  • Public profiles may not reflect current status
  • Information may be outdated or inaccurate
Recommendations:
  • Use as supplementary tool, not primary source
  • Verify findings through multiple methods
  • Comply with Fair Credit Reporting Act (US) if applicable
  • Consider hiring professional background check services
  • Always verify critical information independently

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Troubleshooting

Common reasons for failed results:
  1. Network Issues: Check your internet connection
  2. Rate Limiting: Websites blocking automated requests
  3. Outdated Detections: Site structure changed since last update
  4. Anti-Bot Protection: Cloudflare, CAPTCHA, etc.
  5. Timeout Too Low: Increase with --timeout 15
Solutions:
# Filter to only show detected profiles
python3 -m social-analyzer --username "johndoe" --profiles "detected"

# Increase timeout
python3 -m social-analyzer --username "johndoe" --timeout 20
See the Troubleshooting Guide for detailed solutions.
If you encounter problems:
  1. Check GitHub Issues: https://github.com/qeeqbox/social-analyzer/issues
  2. Search for similar problems - may already be solved
  3. Update to latest version: 
    pip3 install --upgrade social-analyzer
  4. Open a new issue with:
    • Operating system
    • Python/Node.js version
    • Complete error message
    • Steps to reproduce
    • Command you ran
Note: For private module issues, contact the author directly (don’t open GitHub issues).

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Getting Help

Resources:Community:
  • Search existing GitHub issues
  • Check closed issues for solutions
  • Review pull requests for upcoming features
Contributions are welcome!Ways to Contribute:
  • Report bugs and issues
  • Suggest new features
  • Add detection rules for new websites
  • Improve documentation
  • Submit pull requests with fixes
  • Share usage examples and tutorials
  • Test and provide feedback
Getting Started:
  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Test thoroughly
  5. Submit a pull request
See CONTRIBUTING.md in the repository for detailed guidelines.
The standard version is open source and free. However:
  • Some law enforcement agencies use an enhanced detection database
  • The public version differs from the professional version
  • For enterprise licensing or custom features, contact the author directly
  • Private modules are available for specific use cases
Contact: [email protected]

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