Parsing Telegram: Channels, Groups and Audience
Any Telegram campaign starts with a list: where your audience is and who exactly is in it. Building that by hand takes weeks. The parser does the same job from keywords and files the result into bases the other modules pick up immediately.
Three kinds of parsing
Channels — search by keywords and their combinations. The combination “crypto + education” finds channels containing both rather than everything about crypto, which makes the base several times cleaner.
Groups and chats — the same search, but for venues where you can post. These are what chatting and member collection need.
Members — an export of people from the groups you found: name, username, ID. Accounts without a username can be filtered out on the spot so the base does not swell with dead rows.
What you end up with
Three bases in the dashboard: channels, groups and users. Each can be viewed as a table, sorted, cleaned and exported to a file.
The channel base is distributed across your accounts automatically and without overlap, so each one gets its own set and two accounts never land in the same place.
Limits, stated honestly
Telegram does not hand over member lists for every group: in large and closed communities the export is truncated or unavailable. That is a restriction of the messenger itself and cannot be worked around.
Heavy parsing triggers temporary request limits. The system recognises them, waits and resumes from where it stopped instead of starting over.
Keeping the base clean
Junk in the base costs more than it looks: accounts spend their limited actions on empty channels. That is why the tables support both single deletion and bulk cleanup.
A simple routine works: check per-channel stats weekly and drop anything that produced no response in that time.
Choosing keywords
A broad word returns junk: search for “crypto” and you get everything from news aggregators to ad-filled chats. What works is combinations of two or three words that only occur together in your topic.
Add the words your audience uses to describe the problem, not the product. People search for “how to choose” and “what to do if”, not for a category name.
For local business add the city and district. That narrows results sharply and removes national venues where your offer means nothing.
Similar channels and going deeper
Beyond keyword search there is similar-channel collection: the system takes a channel you found and pulls in the ones Telegram considers close to it. A few good finds can grow into a full base.
Recursive traversal goes further — similar channels of similar channels. Useful in narrow niches where direct keyword search returns a dozen venues and stops.
The deeper you go the more noise you collect; by the third level the topic has drifted. One or two levels is usually the sensible depth.
How often to refresh
Channels die: some stop publishing, some close comments, some get blocked. A base collected six months ago is half dead ground.
The practical rhythm is re-running the parser monthly on the same keywords and cleaning out non-responding channels weekly.
User bases need refreshing more often: people leave groups and change usernames, and a message to a contact that no longer exists still spends an account's limit.