This project automates the discovery and evaluation of influencers on X, eliminating slow manual searches and repetitive profile checks. The X Influencer Finder helps teams quickly surface high-fit creators, analyze their metrics, and export clean results for outreach or marketing pipelines.
This automation system scans influencer profiles, extracts public insights, and structures them into actionable data. It removes the repetitive workflow of opening profiles, validating metrics, checking posting behavior, and aggregating results. Users and businesses benefit from faster research cycles, more consistent data quality, and a scalable way to explore X creator audiences.
- Continuously scans profiles based on keywords, hashtags, or niche filters.
- Normalizes metrics for comparison and selection.
- Integrates scheduling and device handling for reliable long-running automation.
- Produces structured exports suitable for analysis or CRM upload.
- Minimizes human effort while boosting research throughput.
| Feature | Description |
|---|---|
| Profile Scanning | Automates navigation to X profiles and extracts public metadata. |
| Engagement Metrics Parsing | Collects follower count, posting rate, and engagement indicators. |
| Keyword-Based Discovery | Finds influencers matching chosen niches or terms. |
| Automated Queue Processing | Manages large batches of profiles using device worker queues. |
| Screenshot & Evidence Capture | Stores screenshots for verification and audit. |
| Retry & Backoff Logic | Adds resilience during slowdowns or temporary UI changes. |
| Proxy & Session Rotation | Reduces friction and distributes network load across tasks. |
| Data Normalization | Cleans and standardizes influencer metrics for ranking. |
| Export to JSON/CSV | Outputs structured influencer records for downstream workflows. |
| Enriched Activity Logs | Records execution, errors, and performance for debugging. |
- Input or Trigger — User supplies keywords, lists of handles, or discovery rules.
- Core Logic — Automation launches on Android devices, opens X, navigates profiles, and extracts visible metrics.
- Output or Action — Structured results and screenshots are saved to the
/outputdirectory. - Other Functionalities — Queued tasks, proxy rotation, and scheduled jobs maintain continuous execution.
- Safety Controls — Rate limits, cooldown timers, and guarded retries prevent device overload and reduce failures.
Language: Python Frameworks: Appilot, UI Automator, FastAPI (optional control API) Tools: Scheduler, queue manager, proxy manager, structured logger Infrastructure: Local devices, 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
- Marketing teams use it to identify niche influencers, so they can accelerate campaign planning.
- Agencies use it to evaluate creator quality at scale, so they can present vetted lists to clients.
- Growth teams use it to discover micro-influencers, so they can optimize outreach strategies.
- Researchers use it to monitor trends, so they can analyze community movements faster.
Does it require API access? No, it works through Android interface automation.
Can it run on multiple devices at once? Yes, it supports horizontal scaling across device workers.
Is configuration customizable? All discovery rules, filters, and thresholds are defined in config files.
How often can it run? You can schedule continuous or one-time scanning depending on resources.
Execution Speed: ~55–65 profile actions per minute on balanced device farm conditions. Success Rate: Approximately 93–94% across long-running jobs with automated retries. Scalability: Supports 300–1,000 Android devices through sharded queues and horizontally scaled workers. Resource Efficiency: Typical worker footprint is ~1 CPU core and 300–400MB RAM per active device. Error Handling: Automated retries, exponential backoff, structured logs, alert hooks, and recovery flows ensure stability during extended runs.
