Как парсить Telegram-каналы: полный гайд

Brief

Step-by-step technical walkthrough for parsing Telegram channels, evaluating post virality, and saving the best posts to Airtable. Uses a custom Apify-style parser and an automation pipeline composed of HTTP requests → JavaScript transforms → Airtable. Demonstrates parser selection and testing, building the automation, data transformations, and common Telegram data pitfalls (images, attachments, mixed records).

Key concepts and components

• Parsers/scrapers
  • Marketplace-style parsers (Apify-like) for Telegram, Instagram, YouTube, Reddit, Google Ads, etc.
  • Payment models: pay-per-result, pay-per-run, monthly rental.
• Parser selection and testing
  • Always inspect the parser’s “all fields” output before automating.
  • Different parsers return different fields (text, media URLs, thumbnails, subscriber counts, view counts, reaction breakdowns).
• Important parsed fields for virality
  • Total reactions, views, subscribers.
  • Typical virality/activity coefficients: reactions/views or reactions/subscribers.
• Telegram-specific parsing issues
  • Media (images) are often returned as separate items/messages; images can be split from the main post text.
  • You must detect and recombine related items (media URLs) into one logical post.
• Automation building blocks
  • Manual trigger (recommended during testing).
  • HTTP request node calling parser/actor endpoint (import cURL provided by platform).
  • Sorting nodes (by date and by engagement metrics).
  • Merge/union node to consolidate arrays.
  • JavaScript transform node for data consolidation and normalization.
  • Airtable create-or-update node to insert/update best posts.

Automation architecture (high level)

1. Manual trigger to start parsing.
2. HTTP request → call parser actor synchronously and retrieve dataset items.
3. Sort parsed items by date (fixes ordering) and later by engagement metrics (Total Reactions).
4. Merge/union items into a single array for downstream processing.
5. JavaScript transform:
   • Combine image URLs published at the same timestamp into a single post object’s MediaURLs.
   • Choose appropriate post ID / reaction totals when media and text were parsed separately (e.g., pick max Total Reactions or the correct base item).
6. Sort by Total Reactions (descending) and limit to top-N posts.
7. Airtable: create-or-update rows, matching on post link (unique key) to avoid duplicates and keep metrics updated.

JavaScript transform responsibilities (examples)

• Detect media-only items that share timestamps with text posts and merge them into the base post.
• Consolidate multiple image URLs into a MediaURLs array attached to the post object.
• Resolve metadata conflicts (e.g., pick max Total Reactions when separate items have different reaction counts).
• Produce Airtable-ready fields (dates parsed, numbers coerced, attachments formatted).

Airtable specifics and gotchas

• Field types matter: make sure date and number fields match Airtable schema. Use Airtable’s Typecast option for date conversion if needed.
• Attachments: Airtable expects attachment objects/arrays — plain URLs will often fail.
  • Wrap URLs in arrays/objects (proper JSON structure) or use a transform that outputs the required attachment format.
• Multiple images:
  • Build logic or formulas to populate multiple attachment slots.
  • Handle missing images by using nulls/placeholders so Airtable does not error.
• Create-or-update matching:
  • Use the post link as the unique key to prevent duplicates and to update existing rows with new metrics.

Testing & debugging advice

• First inspect parser output manually (all fields) — different parsers return different structures.
• Sort parsed items by date to avoid out-of-order results.
• Carefully combine media items and verify which item supplies the canonical metadata (text, reaction totals).
• Double-check field types before sending to Airtable (avoid passing text into numeric fields).
• Expect to iterate — common issues include fewer items returned than expected, images split from posts, and missing text after merging.

Practical guide / checklist (from the demo)

1. Choose and test a parser on the Apify-like marketplace; inspect returned fields via “All fields”.
2. Decide payment model and create a task (actor → task pattern) per account/channel for clarity and reporting.
3. Prepare destination schema in Airtable:
   • Fields: post link, channel name, subscribers, post text, views, total reactions, media attachment slots, publish date, “to post” checkbox.
4. Build the automation:
   • Manual trigger → HTTP request to parser actor (import cURL) → get items.
   • Sort by date → merge items → JS transform to group media into MediaURLs and consolidate metadata.
   • Sort by Total Reactions (desc) → limit to top-N.
   • Create or update Airtable rows (match on post link), use Typecast for dates, and convert media URLs into Airtable attachment objects/arrays.
5. Validate results and fix issues (e.g., choose max total reactions when merging, ensure text and images attach correctly).
6. Add downstream posting automation in the next lesson (reposting top posts to social networks or back to Telegram).

Common pitfalls

• Parsers differ widely in output fields — always test before automating.
• Telegram often returns images as separate messages; detect and recombine into single post objects.
• Airtable attachments require object/array format; direct URLs will fail unless transformed.
• Date/number format mismatches — use Typecast or explicit parsing.
• Automation platform quirks: limits, server load indicators, and occasional missing items — expect to iterate and retest.

Tools and models mentioned

• Apify-like parser marketplace / actor platform
• Airtable (including Typecast)
• Automation platform with HTTP & JS code nodes (n8n-like)
• HTTP request node with importable cURL
• JavaScript code node for custom transforms
• Gemini and OpenAI (O3 / O4) to quickly generate transformation code

Next steps / follow-ups mentioned

• A follow-up lesson will demonstrate reposting automation: selecting the most viral parsed posts (via a “checked” flag) and posting them to social networks or back to Telegram using an agent.
• The presenter will publish corrected code and a polished automation after resolving merge/attachment issues.

Speakers / sources

• Presenting instructor (unnamed)
• Olga — student/developer who built the custom Telegram parser used in the demo
• Mentions: Tolya, Kolya (helpers/colleagues)
• Platforms/tools referenced: Apify (parser marketplace/actors), Airtable, Gemini / OpenAI, automation platform (HTTP & JS nodes)