Telegram Article Scraper Bot, Scrapes content, Automatically scrapes articles from selected websites and shares them, Ideal for content curation and news sharing
This project automates the flow of collecting fresh articles from predefined sources and sending them straight into Telegram channels or chats. It cuts out the constant tab-hopping and manual copy-paste that usually slows down content curation. With the Telegram Article Scraper Bot, Scrapes content, Automatically scrapes articles from selected websites and shares them, Ideal for content curation and news sharing, you get a hands-off way to keep audiences updated.
This automation handles routine article gathering—fetching, parsing, formatting, and forwarding content to Telegram. It replaces a repetitive workflow where someone would normally monitor sites and manually share updates. The tool helps creators, community managers, and businesses deliver consistent, real-time content without babysitting the process.
- Reduces the time spent manually browsing and sharing articles.
- Keeps Telegram channels active with reliable, scheduled updates.
- Ensures content quality and formatting remain consistent.
- Supports scalable workflows for newsrooms or community managers.
- Minimizes human error when dealing with high-volume feeds.
| Feature | Description |
|---|---|
| Scheduled Scraping | Runs timed scraping cycles using an internal scheduler. |
| Smart URL Scanner | Detects article sections, metadata, and structured content. |
| Telegram Auto-Sharing | Sends formatted articles directly to Telegram chats or channels. |
| Content Deduplication | Avoids reposting previously shared links or articles. |
| Proxy Management | Routes requests through rotating proxies for stability. |
| HTML-to-Text Parser | Converts pages into clean, readable message content. |
| Error & Retry Logic | Recovers gracefully from timeouts or missing selectors. |
| Configurable Sources | Lets you define custom URLs, categories, or domains. |
| Logging & Reporting | Tracks activity and issues with timestamped logs. |
| Lightweight Worker Mode | Runs efficiently on low-power or mobile-oriented environments. |
- Input or Trigger — A scheduler kicks off scraping cycles at set intervals.
- Core Logic — Pages are fetched, parsed, cleaned, and transformed into structured article snippets.
- Output or Action — The bot posts the content to Telegram using bot credentials or channel tokens.
- Other Functionalities — Proxy rotation, duplicate filtering, and formatting helpers enhance stability.
- Safety Controls — Rate limiting, retries, and validation ensure reliable long-running execution.
Language: Python
Frameworks: Lightweight async scraping libraries, automation schedulers
Tools: Appilot, UI Automator, optional ADB-less pipelines
Infrastructure: Local runners, containerized jobs, or distributed worker queues
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
- News curators use it to auto-share daily articles so they can keep channels active with minimal effort.
- Marketing teams use it to track niche publications and forward updates to internal Telegram groups.
- Community managers use it to deliver timely content to members and maintain engagement.
- Research teams use it to gather topic-specific articles automatically for review.
- Small publishers use it to mirror website updates into Telegram without manual posting.
Does it support multiple websites?
Yes, you can list as many sources as needed in the config file.
Can it post to multiple Telegram channels?
Absolutely—just add multiple chat IDs or tokens.
Is scheduling flexible?
You can configure time intervals, cron-like expressions, or one-off triggers.
Does it detect repeated content?
Yes, deduplication ensures no accidental reposting.
Is it suitable for long-running tasks?
Yes, thanks to retry logic, structured logging, and low resource usage.
Execution Speed: Processes 20–30 article fetches per minute under typical device farm conditions.
Success Rate: Around 93–94% success across long-running scraping cycles with retries enabled.
Scalability: Can distribute scraping tasks across 300–1,000 Android devices using sharded queues and horizontally scaled workers.
Resource Efficiency: Targets ~1 CPU core and 200–350 MB RAM per worker, depending on concurrency.
Error Handling: Includes exponential backoff, structured logs, automated retries, and recovery flows to maintain stability over multi-hour or multi-day runs.
