A lightweight automation system that scans Facebook content for specified keywords and triggers real-time alerts whenever new matches appear. The Facebook Keyword Monitor reduces repetitive manual checking and delivers up-to-date insights for community managers, marketers, and researchers.
This automation continuously watches Facebook feed elements, post lists, or comment threads for the presence of specific keywords. It removes the manual burden of scrolling, refreshing, and filtering content, replacing it with a reliable, scheduled workflow. Users benefit from faster awareness, improved responsiveness, and an auditable stream of keyword-triggered events.
- Uses stable device automation to read UI elements predictably.
- Reduces time spent manually searching through high-volume Facebook pages.
- Runs autonomously with scheduled intervals and retry logic.
- Handles noisy content using configurable keyword filters and thresholds.
- Works across multiple devices in parallel using sharded tasks.
| Feature | Description |
|---|---|
| Keyword Tracking Engine | Scans Facebook UI elements for target words with configurable matching rules. |
| Background Scheduler | Executes monitoring tasks at defined intervals without user intervention. |
| Multi-Device Scaling | Supports concurrent monitoring across numerous Android instances. |
| Appilot Navigation | Uses Appilot flows for stable movement within Facebook’s UI. |
| UI Automator Fallback | Provides alternate UI traversal when Appilot signals are limited. |
| Notifications & Alerts | Sends structured alerts when keywords are detected. |
| Result Exporter | Outputs JSON/CSV logs of keyword matches for external systems. |
| ADB-less Operation | Runs without direct ADB commands, improving sandbox safety. |
| Error & Retry Layer | Recovers from navigation failures or unexpected UI changes. |
| Configurable Filters | Allows keyword weighting, exclusion terms, and relevance scoring. |
- Input or Trigger — User-defined keyword list and monitoring schedule.
- Core Logic — Automated device sessions navigate Facebook UI and read text nodes.
- Output or Action — Generates alerts, exports logs, and stores match data.
- Other Functionalities — Handles scrolling, pagination, and feed refresh operations.
- Safety Controls — Rate limiting, sandboxed sessions, and automatic error recovery.
Language: Python Frameworks: Appilot, UI Automator Tools: Scheduler, queue workers, logging utilities Infrastructure: Distributed Android device farm or emulator cluster
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
- Agencies use it to automate trending-topic detection so they can respond to conversations immediately.
- Community managers use it to identify sensitive keyword mentions so they can moderate discussions quickly.
- Market researchers use it to capture keyword-based sentiment so they can analyze patterns over time.
- Support teams use it to track complaint-related terms so they can prioritize customer outreach.
Does it require advanced configuration? Only initial keywords and schedule intervals; defaults handle the rest.
Can it run on multiple devices? Yes, it supports horizontal scaling through sharded workers.
Does it store historical results? All matches are logged to JSON/CSV for long-term analysis.
How accurate is the keyword matching? It uses robust UI text extraction with configurable filters for precision.
Execution Speed: Typically 30–45 UI actions per minute across standard device clusters. Success Rate: ~93–94% over long-running sessions with automatic retries. Scalability: Effectively scales to 300–1,000 Android devices through distributed queues. Resource Efficiency: ~1 vCPU and 350–500 MB RAM per worker/device session. Error Handling: Includes structured logs, retry with backoff, fallback navigation modes, and automated recovery flows.
