TeleTraff: How to run a Telegram parser?

Parsers collect available data from the sources you point them at and save it as datasets for further work. Almost every scenario starts with a dataset: inviting, messaging, story views and reactions all work from one.

The module overview lives on the parsers page.

What you need before launch

Accounts with proxies — collection runs on their behalf.
A source your accounts can reach: a channel, group or chat.
A clear idea of which dataset type you need. This is the main decision, and getting it wrong costs the most.

Which dataset type to choose

Result type	Where it goes next
Members and commenters	inviting, messaging, story views
Channels and groups	AI commenting, mass reactions
Messages	topic analysis and source selection

How to launch

Choose the source and the result type.

Parser settings: the source, the type of data collected and the selection filters.
Set the filters.

Filters narrow the selection down to the records you will actually use. The tighter the filter, the higher the share of usable targets in the final dataset — and the fewer limits you waste in the next module.
Check the pre-launch plan and available accounts.

Start the collection and watch the status.

Status	What it means
Queued	the job was accepted and waits for a free account
Running	collection is in progress, the record counter grows
Done	collection finished completely
Partial	part of the data was collected, the rest was unavailable
Awaiting continuation	collection is paused and will continue later
Has errors	some sources did not return data

Open the saved dataset and assess it.

Look beyond the record count at how usable they are: the share of closed profiles differs several-fold between sources.

A safe test run

Take one account and a small open chat it can reach.
Collect members with no filters.
Run the dataset through inviting on 10–15 targets.
Look at the success share: that is the real quality of the source, not the dataset size.

Why there are fewer records than expected

Some data is not available to the account doing the collecting.
The source limits what it returns to everyone.
Filters removed more than expected.
Duplicates are not saved twice.

Common mistakes

Mistake	What happens
A dataset of the wrong type	the module will not accept it during configuration
Judging a dataset by record count	size does not equal usability
Collecting without filters for a narrow task	the next module’s limits go on irrelevant targets
One source for a whole campaign	the audience comes out uniform and burns out fast

Parsing works with personal data even when it is public.

Collect only what the task genuinely needs, and do not keep it longer than necessary.
Data being open does not make it yours: data-protection law applies regardless of where the data came from.
The panel collects what is available — what the source already shows the account. It does not crack open closed data.
Telegram’s rules take precedence over job settings.

See Responsible use.

FAQ

How does a member dataset differ from a channel dataset? The first contains people and suits inviting and messaging. The second contains venues and suits commenting and reactions.

Can one dataset be used in several jobs? Yes, a saved dataset is available for repeated selection.

Why does the dataset hold fewer records than the source has members? What is available to the account is collected, duplicates are not repeated, and filters remove part of the records.

Does the parser collect private data? No. It collects what the source already shows the account.

What does the “Partial” status mean? Part of the data was collected and the rest turned out to be unavailable. This is not a failure.

How to run inviting in Telegram? — the main consumer of people datasets
How to run messaging in Telegram? — the second consumer
How to run AI commenting in Telegram? — consumer of channel lists
Responsible use

Official module page: Telegram parsers