This project automates the collection and monitoring of reactions on Facebook posts directly from Android devices. The Facebook Reaction Tracker simplifies repetitive engagement-checking tasks and provides structured output for analytics and reporting. It helps creators, analysts, and businesses understand reaction trends without manual checking.
This automation tool captures reaction summaries, counts, and user engagement data from targeted Facebook posts using Android automation workflows. It removes the repetitive task of opening posts, expanding reaction lists, scrolling through users, and extracting metrics. Users benefit from consistent, fast, and reliable data capture suitable for analytics dashboards and growth insights.
- Eliminates manual scrolling through large reaction lists.
- Captures reaction metrics at scheduled intervals for trend tracking.
- Works on real Android devices using ADB-less and Appilot-friendly automation.
- Produces structured JSON and CSV outputs ready for downstream analysis.
- Enhances consistency by removing human error in repetitive monitoring tasks.
| Feature | Description |
|---|---|
| Post Reaction Scraping | Automates navigation to a specific post and extracts full reaction details. |
| Scheduled Monitoring | Uses a scheduler to repeat scans at set intervals. |
| Reaction Type Breakdown | Separates counts for Like, Love, Haha, Wow, Sad, and Angry. |
| User List Extraction | Collects IDs and names of users who reacted. |
| Incremental Updates | Captures only new reactions after each run. |
| Multi-Device Execution | Runs on multiple Android devices with queue-based coordination. |
| Headless Mode | Executes tasks silently without requiring screen interaction. |
| Appilot-based Navigation | Leverages Appilot’s gesture and UI handling for reliability. |
| Error Recovery | Automatically retries failed tasks with exponential backoff. |
| Exportable Reports | Saves data to JSON and CSV for downstream analytics. |
- Input or Trigger — User provides a post URL or scheduler triggers a periodic scan.
- Core Logic — Automation navigates the Facebook app, opens reaction panels, and collects structured data.
- Output or Action — Results are stored in JSON and CSV files for analytics.
- Other Functionalities — Supports incremental updates, device pooling, and session-based operation.
- Safety Controls — Rate limits, UI validation, error handling, and controlled navigation steps prevent misfires.
Language: Python Frameworks: UI Automator, Appium, Appilot navigation helpers Tools: Scheduler, structured logger, proxy manager, configuration loader Infrastructure: Local devices, device farm setups, distributed task queue
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
- Content creators use it to track engagement on new posts so they can optimize posting strategies.
- Marketing teams use it to measure campaign performance automatically and produce daily summaries.
- Analysts use it to gather long-term reaction trends for dashboards and forecasting models.
- Social media managers use it to monitor competitor reactions at scale.
- Businesses use it to track customer sentiment via reaction changes over time.
Does this require root access? No, it works on standard Android devices using UI automation.
Can it run on multiple devices simultaneously? Yes, it supports sharded execution for device farms.
Does it work on the Facebook Lite app? Most automation flows target the main Facebook app for consistency.
Can it detect new reactions only? Yes, incremental update logic is supported.
Is internet connection required? Yes, as the automation interacts with live Facebook content.
Execution Speed: Approximately 40–60 reaction scans per minute on mid-range device farms. Success Rate: Around 93–94% success across long-running scheduled jobs with retries enabled. Scalability: Supports 300–1,000 Android devices using sharded queues and horizontally scaled workers. Resource Efficiency: ~15–20% CPU and 300–450MB RAM per worker; 5–8% CPU per active device session. Error Handling: Automated retries, exponential backoff, structured logs, alert hooks, and state recovery built in.
