This project automates the collection of publicly visible Bumble profile data using Android UI automation. The Bumble Profile Scraper streamlines repetitive discovery actions, reduces manual effort, and enables structured output for analysis or research.
This automation tool simulates human navigation within the Bumble app to capture profile information such as bios, interests, prompts, and images. It removes the tedious manual process of opening profiles one by one, making data gathering more consistent and efficient. Businesses, researchers, and developers benefit from a repeatable workflow that outputs clean, structured data.
- Eliminates manual swiping and tapping through UI-driven automation.
- Ensures consistent extraction of visible data across large device fleets.
- Supports throttling, randomized timing, and human-like interaction.
- Integrates queue-based job scheduling for distributed device workloads.
- Provides structured JSON/CSV results for downstream processing.
| Feature | Description |
|---|---|
| UI Automation Engine | Uses Appilot/UI Automator logic to navigate Bumble profiles. |
| ADB-Less Interaction | Runs device actions without requiring direct ADB command flows. |
| Touch & Scroll Simulation | Generates human-like gestures to avoid predictable patterns. |
| Profile Data Extraction | Captures visible bios, prompts, interests, and match details. |
| Image Scraping Workflow | Extracts accessible profile image assets with attribution. |
| Proxy & Network Rotation | Manages network paths to reduce session clustering. |
| Session Persistence | Maintains stable login and cookie handling across runs. |
| Error & Retry Handling | Automatically retries failed actions with backoff logic. |
| Distributed Task Runner | Enables multi-device scraping through sharded queues. |
| Result Normalization | Cleans and structures scraped fields for export-ready output. |
- Input or Trigger — A job specifying device ID, number of profiles, and extraction parameters.
- Core Logic — The automation engine opens Bumble, scrolls through profiles, and captures visible information.
- Output or Action — Each completed profile is stored as structured JSON and later compiled into CSV reports.
- Other Functionalities — Optional screenshot capture, network rotation, throttled action pacing, and environmental logging.
- Safety Controls — Randomized timings, gesture variability, and configurable rate limits to simulate human behavior.
Language: Python Frameworks: Appilot, UI Automator, FastAPI (optional APIs) Tools: Task schedulers, device orchestrators, queue systems Infrastructure: Local device labs, cloud device farms, containerized workers
automation-bot/
├── src/
│ ├── main.py
│ ├── automation/
│ │ ├── tasks.py
│ │ ├── scheduler.py
│ │ └── utils/
│ │ ├── logger.py
│ │ ├── proxy_manager.py
│ │ └── config_loader.py
├── config/
│ ├── settings.yaml
│ ├── credentials.env
├── logs/
│ └── activity.log
├── output/
│ ├── results.json
│ └── report.csv
├── requirements.txt
└── README.md
- Researchers use it to gather public profile data at scale so they can analyze trends and behaviors.
- Marketing teams use it to study audience segments so they can refine targeting strategies.
- Automation engineers use it to benchmark UI automation flows so they can validate device performance.
- Developers use it to prototype recommendation engines so they can test matching algorithms.
Does it require rooting the device? No, it leverages standard Android automation APIs.
Can it run on multiple devices? Yes, it supports sharded queues and distributed workers.
Does it store any credentials? Credentials are stored in a separate environment file managed by the user.
Is rate limiting adjustable?
Yes, timing and pacing parameters can be configured in settings.yaml.
Can I export results? Results are automatically saved as JSON and CSV.
Execution Speed: Typically 18–25 actions/min under standard device farm loads. Success Rate: Averaging ~93–94% on long-running tasks with retries enabled. Scalability: Designed to handle 300–1,000 Android devices through horizontally scaled worker nodes. Resource Efficiency: ~1–1.5 CPU cores and 300–450MB RAM per worker per device. Error Handling: Automated retries with exponential backoff, structured logs, alert hooks, and crash recovery flows.
