What I learned trying to index public Telegram communities

Austin Reynolds — Wed, 30 Sep 2026 23:17:33 +0000

Telegram has an enormous number of public communities and almost no structured way to discover them. I spent a while looking at what it actually takes to build an index of them, and most of the difficulty is not where you would expect.

The metadata problem

Telegram's public API exposes very little structured metadata about a public channel or group. You generally get a title, a description, an invite target, and an approximate member count. There is no reliable topic field, no canonical category, and no dependable language tag.

That matters more than it sounds. If you want to organise a catalogue by subject, you have to infer the subject yourself — from the description text, from the title, from what the community posts. Every one of those signals is noisy.

Entity types are not interchangeable

The first design decision is that channels, groups and bots are three different things:

Channels broadcast. One publisher, many readers.
Groups converse. Everyone can post; the culture is entirely a function of moderation.
Bots are tools, not places.

Treating them as a single "community" list makes the catalogue much less useful, because a reader looking for a discussion group does not want a broadcast feed.

Language is the filter people skip

Most of the Telegram ecosystem is not English. A catalogue that ignores language will happily surface a very active community that the reader cannot read. Adding a language dimension is cheap compared to the usefulness it adds.

Staleness is the real enemy

Communities go quiet, rename, or turn private. An index that never re-checks entries degrades into a list of dead invite links surprisingly fast. This is the least glamorous part of the problem and probably the most important: you need a re-verification loop, not just an ingestion pipeline.

What the resulting shape looks like

An index that is actually usable tends to converge on the same handful of features: a topic taxonomy with an editorial hand, tag and language filters, entity-type separation, a per-entry description, and an activity snapshot.

VIEW is one example built along those lines — it indexes public Telegram channels, groups and bots with category, tag and language filters and a member-count snapshot per entry. Browsing needs no account.

Two honest caveats, because they matter if you are evaluating it: the catalogue is broad and includes a large adult-content category, so it is not appropriate for every audience; and no index of this kind is exhaustive. Treat any of them as a starting point rather than a definitive list.

Takeaway

If you are building anything that indexes user-generated communities, budget your time for re-verification rather than for ingestion. Getting things in is easy. Keeping them accurate is the whole job.

How to Tell a Good Telegram Channel From a Bad One: Notes From 16,548 Published Entries

Austin Reynolds — Tue, 22 Sep 2026 15:11:48 +0000

A Telegram link lands in a group chat. The channel has 80,000 subscribers, a clean logo, and a pinned post that looks like a press release. You join. Three weeks later it's a crypto funnel, and the "news" it posts is affiliate copy.

Nothing in Telegram warned you. Not because Telegram doesn't know — it often does — but because nothing in the product is designed to answer the question you actually have: should I trust this?

In-app search is built to find a channel you already have a name for. There is no review layer, no reliable category structure, and no history. So most people decide on two signals — subscriber count and how professional the avatar looks — and both are trivial to fake.

I build VIEW, a directory that indexes public Telegram channels, groups and bots, classifies them, and publishes what passes its checks. The interesting part of that work turned out not to be the directory but the dataset behind it. Here is what 16,548 published entries look like as of 22 September 2026.

What I measured

The index currently holds 47,469 public entities collected from Telegram's public surface. Collection is already selective: personal accounts are rejected (an ordinary user isn't a channel, group or bot), targets below a minimum audience are skipped, and a bot has to clear a minimum monthly-active-user count to qualify at all.

Of what gets through, 16,548 entries are published and searchable. The rest don't make the bar — 18% of the index sits in a hidden bucket that is 99.9% entries scoring under 3 out of 10, and the remainder are drafts waiting on classification or sitting below the publication threshold. Across the index as a whole: 76% channels, 22% groups, 2% bots.

Every published entry is classified by a model into a category tree and given a summary in each of the site's three languages. Entries that Telegram itself flags as scam or fake are withheld from the public side by the same automatic rules.

Three caveats before the numbers: this is one index rather than all of Telegram; every count is a 22 September 2026 snapshot that moves by a few entries an hour; and the classification is automatic, so it can be wrong at the margins.

With that out of the way, five findings that changed how I look at a channel before joining one.

1. Verification is rare — only 3.1% have it

517 of 16,548 entries carry Telegram's verified badge. That's it. The other 97% are unverified, which makes the badge one of the few signals on the platform that actually means something: it says somebody at Telegram looked at the identity behind the channel. Its absence tells you nothing was checked — not that anything is wrong.

The practical move is small and most people skip it. When you find a channel through a link or a listicle, confirm that the @username matches exactly. Impersonation is a lookalike-character problem, not a design problem: @news_channel and @news_channeI are different accounts with the same avatar, and the verified badge is the only thing that breaks the tie.

2. One in fifteen carries a restricted flag — and people never check

1,093 published entries (6.6%) carry Telegram's restricted flag. That's not a rare edge case; it's common enough that checking it should be a habit rather than a precaution. Telegram does surface the flag in the channel's own interface, but it's easy to miss — a thin line of small text that most people never go looking for.

Restricted is also the flag you are most likely to encounter of the three. In the full index I found 4,187 restricted entries against 15 flagged as scam and 3 as fake.

3. Bigger is not safer. It's the opposite.

This is the finding I did not expect. Splitting published entries by audience size and counting how many carry the restricted flag:

Audience	Entries	Share of directory	Restricted	Restricted rate
under 1k	1,260	7.6%	14	1.1%
1k – 10k	7,024	42.4%	210	3.0%
10k – 100k	6,419	38.8%	601	9.4%
100k and up	1,845	11.1%	268	14.5%

The restricted rate rises about thirteenfold from the smallest bucket to the largest. Subscriber count is not evidence of trustworthiness; if anything it correlates with the opposite.

I can't separate the two plausible causes from this data. Either reach attracts gray operators (the economics of a 200k audience are worth the risk of a flag), or Telegram applies the flag more aggressively to channels with reach — or both. What I can say is that the correlation is monotonic and large enough to matter for how you decide.

The top bucket is also where the verified channels live: 287 of those 1,845 are verified (15.6%), against zero in the under-1k bucket. So size amplifies both signals. It is an amplifier, not a signal.

4. A clean label only ever means "not caught yet"

Across the raw index, 15 entries are flagged scam and 3 are flagged fake. Zero of them are published — the pipeline withholds them, and Telegram's own reviewer flags are the reason they were caught at all.

That's the point worth taking to any channel you evaluate anywhere else. A directory listing, a "best channels" listicle, a viral screenshot: none of them are evidence. Absence of a warning is not a warning of absence. A second opinion from outside the platform costs nothing and catches what a single view inside it cannot.

5. English is 23% of what's actually there

Content languages across published entries: Chinese 12,382 (75%), English 3,726 (23%), Russian 823 (5%), then Japanese 470, Indonesian 389, German 184, Persian 165, Arabic 152. Entries carry an average of 1.17 languages, so bilingual channels are the exception rather than the rule.

Read that against the lists you find in English. They are not a smaller sample of Telegram; they are a different slice of it, and usually a narrow one. If you read a second language, the discovery problem changes shape completely.

The five-minute checklist

Everything above compresses into five things to check before you join:

Verified badge — present or not, and does the @username match the source you found it through, character for character?
Restricted or scam banner — one in fifteen published entries has one. Check it before you rely on the channel, not after.
The last ten posts — original commentary or pure reposts and links? The ratio tells you whether a human is actually behind it.
Growth sanity — an 80,000-subscriber channel with three posts, no discussion group and a two-week history is a red flag, not an achievement.
Category risk profile — gambling, gray-market and piracy categories carry a different baseline. A technology channel and a "signals" channel are not the same bet even when they look equally polished.

If you want the lookup done for you, the directory is free and needs no account: view.tg. How entries get in — and what gets withheld — is documented in the listing standards.

Method and limits, stated plainly. One index, not the whole of Telegram. A snapshot that moves daily. Automatic classification that mislabels things at the margins. And a correlation between audience size and restricted flags that I can report but cannot fully explain. I would rather you read this as a set of habits than as a ranking.

Subscriber counts measure attention. They have never measured trust.

DEV Community: Austin Reynolds