Social Analyzer is an open-source OSINT (Open Source Intelligence) tool designed to locate and analyze a person’s digital footprint across more than 1,000 social media platforms. Built with Node.js and Python, it provides a unified interface—CLI, API, and web app—to search for profiles using usernames, emails, or name permutations. The tool employs multiple detection techniques including OCR, metadata extraction, and pattern recognition to reduce false positives, assigning each found profile a confidence score from 0–100 (No-Maybe-Yes). It is actively used by law enforcement agencies in resource-limited regions and supports advanced features like cross-metadata correlation, force-directed graphs, and automated website filtering by country or category. Its modular design allows integration into broader forensic workflows and supports both local execution and scalable deployments via Docker.
What You Get
- Multi-platform profile search - Searches 1000+ social media sites including Facebook, Gmail, and Google using username, email, or name permutations with automated detection.
- Confidence-based detection scoring - Profiles are rated 0–100 (No-Maybe-Yes) using multiple detection techniques to minimize false positives.
- Metadata and pattern extraction - Extracts metadata, URLs, and patterns from detected profiles using Qeeqbox OSINT modules for deeper analysis.
- Visualized profile graphs - Generates force-directed graphs to map relationships between extracted metadata and patterns.
- Web, CLI, and API interfaces - Offers a graphical web app (http://0.0.0.0:9005/app.html), Node.js CLI, and Python module for flexible integration.
- Screenshot and description capture - Automatically captures profile screenshots, page titles, and website descriptions for evidence collection.
- Custom search filters - Filter results by country (us, br, ru), website type (adult, music), top Alexa-ranked sites, or custom lists.
- JSON output and log dumping - Saves analysis results as structured JSON files and logs to directory or terminal with prettified formatting.
- Proxy, timeout, and user-agent customization - Configure HTTP headers, timeouts (default 10s), implicit waits, and custom user agents for stealthy probing.
- Multi-profile correlation - Search multiple usernames at once (e.g., “johndoe,janedoe”) to find connections across platforms.
- Re-check failed profiles - Automatically retries failed profile checks to improve detection accuracy.
Common Use Cases
- Building an OSINT investigation workflow for cyberstalking cases - Law enforcement uses Social Analyzer to trace perpetrators by correlating usernames across platforms, extracting metadata from profile pages, and visualizing connections with force-directed graphs.
- Creating a threat intelligence feed for phishing investigations - Security teams use the tool to identify fake profiles impersonating employees by matching email patterns and profile metadata from 1000+ sites.
- Problem: Finding a suspect’s hidden social accounts → Solution: Use name permutations + top Alexa sites - When only a partial username is known, Social Analyzer generates combinations and checks high-traffic platforms first to maximize detection rate.
- Team: Digital forensics analysts managing cross-platform investigations - Analysts use the Python module to automate profile checks in bulk, integrate results into SIEM tools via JSON output, and filter for high-confidence (good) profiles.
Under The Hood
Qeeqbox Social Analyzer is a JavaScript-based tool designed for analyzing and detecting social media profiles across various platforms. It integrates web scraping, OCR processing, and pattern matching to provide comprehensive user account identification and data visualization. The system supports both fast and slow scanning modes, offering flexibility in performance and analysis depth.
Architecture
The project follows a modular monolithic architecture with clear separation of concerns between core logic, API interactions, and visualization components. Each functional area is encapsulated in dedicated modules to ensure maintainability and scalability.
- Modular design with distinct files for user profile detection, string analysis, and data visualization
- Layered approach separating data processing from external API integrations and UI components
- Helper modules that support common operations across different parts of the system
Tech Stack
The tool is built primarily using JavaScript and Node.js, with Express framework powering its web services. It leverages a wide range of NPM packages for scraping, language detection, and automation.
- JavaScript/Node.js as the primary runtime with Express for backend services
- Extensive use of Selenium WebDriver and Tesseract.js for browser automation and OCR capabilities
- Python integration for additional processing and extensibility
- Docker support enabling containerized deployment and cross-platform portability
Code Quality
Code quality is moderate, with basic test coverage and some inconsistencies in naming and structure. Error handling is present but not uniformly applied across all modules.
- Basic test suite with minimal automation and validation coverage
- Extensive use of try/catch blocks for error handling in core modules
- Inconsistent code style and naming patterns observed in several files
- Some duplicated logic and limited documentation or inline comments
What Makes It Unique
This tool distinguishes itself through its adaptive scanning logic, OCR integration, and extensible architecture that supports new platforms without code changes.
- Dual scanning modes (fast and slow) tailored for different performance and depth requirements
- Built-in OCR support using Tesseract.js to extract text from screenshots for image-based content detection
- Extensible site definition system that allows adding new platforms without modifying core logic
- Multi-stage retry mechanism with parallel execution limits to improve reliability and resource management