Channel
@zabanvarzi
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
1,964subscribers
+7 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1001372816283 |
|---|---|
| Type | Channel |
| Username | @zabanvarzi |
| Created | Between 1 April 2018 and 31 August 2021 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 18 September 2026 |
| Measurements held | 11 |
| Confirmed unchanged | 1 time, most recently 18 September 2026 |
| On Telegram | t.me/zabanvarzi |
Education — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 16 September 2026 and assigned it the closest of 31 fixed categories, at 90% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 18 Sept 2026, 06:15 | 1,964 | -3 |
| 14 Sept 2026, 11:01 | 1,967 | +2 |
| 10 Sept 2026, 17:02 | 1,965 | -1 |
| 29 Aug 2026, 18:46 | 1,966 | +2 |
| 26 Aug 2026, 20:15 | 1,964 | -1 |
| 20 Aug 2026, 06:48 | 1,965 | +1 |
| 17 Aug 2026, 10:05 | 1,964 | -1 |
| 14 Aug 2026, 02:35 | 1,965 | +5 |
| 10 Aug 2026, 11:52 | 1,960 | +3 |
| 6 Aug 2026, 06:33 | 1,957 | no change |
| 6 Aug 2026, 04:18 | 1,957 | first reading |
20 posts held, back to 14 June 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 2 pages of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 7 August 2026. An engagement rate over an empty window would be a number about nothing.
Measured directly from 6 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
96 reactions across 19 posts, in 4 distinct kinds. The most used accounts for 52.1% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 50 | 52.1% | |
| 👍 | 38 | 39.6% | |
| 👎 | 7 | 7.29% | |
| 😢 | 1 | 1.04% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 20 of the 20 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 108 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 14 June 2026 to 7 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
🍁 آیا ترامپ «کابینهاش را زیر اتوبوس خواهد انداخت»؟ عبارت «کسی را زیر اتوبوس انداختن» ترجمۀ تحتاللفظی از اصطلاح زیر است، که معنای آن را از فرهنگهای کمبریج و مریام ــ وبستر آوردهام. در فارسی، در برابر این اصطلاح «کسی را فدای خود کردن»، «کسی را قربانی کردن»، «زیر پای کسی را خالی کردن» و نظیر آن بهکار میرود. throw sb under the bus (idiom): Cambridge Dictionary: to do sth harmful to sb else in order to gain an ad…
❤4👍1😢1
🍁 Why is eyewitness testimony not always reliable? ✍🏻 Julia Wood & Ann Schweitzer: Ronnie Bullock was serving a sixty-year sentence for kidnapping and raping a woman when DNA tests revealed that he could not have committed the crime. Like a number of other prisoners, Bullock was convicted largely on the basis of eyewitness testimony. “That’s the man; I’ll never forget his face” tends to be highly convincing to juro…
❤2
🍁 شب ابوالحسن نجفی 06:23 علی دهباشی 07:45 علیرضا انوشیروانی (ضبطشده) 25:31 نصرالله پورجوادی 48:56 حسین معصومی همدانی 01:12:53 سخنرانی زندهیاد ابوالحسن نجفی در شب نصرالله پورجوادی (۱۳۹۳) 01:29:44 پیام ارسالی علی صلحجو 01:51:33 مروری بر کتابهای ابوالحسن نجفی 01:54:57 امید طبیبزاده 🎥 از: کانال مجله بخارا (مرداد ۱۴۰۵)
❤4👍3👎1
🍁 شب ابوالحسن نجفی نهصدوشصتویکمین شب از شبهای بخارا به یادبود ابوالحسن نجفی اختصاص یافته است. این نشست با سخنرانی نصرالله پورجوادی، حسین معصومی همدانی، علی صلحجو، مجید ملکان، امید طبیبزاده، علیرضا انوشیروانی و علی دهباشی برگزار میشود. ابوالحسن نجفی (۱۳۰۶ ــ ۱۳۹۴)، فرهنگنویس، مترجم، ویراستار، عروضدان و عضو پیوستهٔ فرهنگستان زبان و ادب فارسی، در سال ۱۳۰۶ در نجف اشرف چشم به دنیا گشود و در سال ۱۳۹۴ در تهران از …
❤3
به مناسبت انتشار کتاب «گنجواژهٔ فارسی» (۱۴۰۵) تألیف بهروز صفرزاده، که از سوی نشر کتاب سده منتشر شده است، شصتوچهارمین نشست از سلسله نشستهای عصر سهشنبههای بخارا به نقد و بررسیِ این کتاب اختصاص یافته است. 00:05 علی دهباشی 02:57 بهروز صفرزاده 17:41 هومن عباسپور 26:53 سعید لیان 47:33 بهروز صفرزاده 🎥 از: کانال مجلۀ بخارا (تیرماه ۱۴۰۵) #️⃣ #نشست #کتاب #فرهنگ
❤1
🍁 Labels Shape Perceptions ✍🏻 Julia Wood & Ann Schweitzer: When we label someone, we focus attention on particular aspects of a person and that person’s activities, and we necessarily obscure other aspects of who that person is. We might define a person as an environmentalist, a teacher, a gourmet cook, or a father. Each definition directs our attention to certain aspects of the person. We might talk with the envi…
❤3
🍁 The effectiveness of persuasive communications ✍🏻 Elliot Aronson et al: The effectiveness of persuasive communications depends on who says what to whom. Who: The Source of the Communication •Credible speakers (e.g., those with obvious expertise) persuade people more than speakers lacking in credibility (Hovland & Weiss, 1951; Schwarz, Newman, & Leach, 2016). • Attractive speakers (whether due to physical or per…
👍2
🍁 واژهزدگی یا عملگرایی؟ ✍🏻 حسین انتظامی: بعضیها فکر میکنند اگر اسم جدید یا قلمبهسلمبه استفاده کنند کار هم تغییر میکند. چند سالی است تب «اقتصاد خلاق و صنایع فرهنگی» به جان مدیران نوگرای فرهنگی افتاده و اصرار به استفاده از این عبارت دارند (مدیران سنتی که البته راحتتر از این حرفها هستند!) مشکل از کجا بهوجود میآید؟ یک نفر در یک مقالۀ انگلیسی، واژه یا عبارتی جدید میبیند. سپس بر اساس «فیلشناسی در تاریکی» همان…
❤3
🍁 دروغ تا کی؟ یاوه تا چند؟ آیا هر واژۀ فرهنگستان ۶۴ میلیون تومان آب خورده است؟ ✍🏻 عباس سلیمی آنگیل ویدیویی در رسانههای همگانی دستبهدست میشود که در آن دو کارشناس (بر من روشن نشد که در چه کاری شناسا هستند) گفتوگو میکنند و یکیشان میگوید: «هر واژۀ فرهنگستان ۶۴ میلیون تومان آب خورده است!» از فرهنگستان زبان و ادب فارسی خردهها میتوان گرفت، اما آنچه این کارشناسواره میگویند آشکارا نادرست است و جز یاوه نامی ندار…
👍4👎3❤2
🍁 Dehumanizing The Enemy: Justifying Cruelty ✍🏻 Elliot Aronson et al: A sad, though universal, phenomenon is that all cultures are inclined to dehumanize their enemies by calling them cruel names and regarding them as “vermin,” “animals,” “brutes,” and other nonhuman creatures. During World War II, Americans referred to the German people as “krauts” and portrayed them as brutes; they called the Japanese people “Jap…
❤3
🍁 وندافزایی به ستاک فعل در زبان فارسی: رویکردی شناختی (۱۴۰۵) ✍🏻 پارسا بامشادی: وندافزایی یکی از فرآیندهای اصلی و پرکاربرد واژهسازی در زبان فارسی است. کتابِ پیش روی شما دربردارندۀ ایده و انگارهای نو برای معناکاویِ فرآیند وندافزایی در زبان فارسی و بهطور مشخص، وندافزایی به ستاک فعل است که میکوشد با ایجاد پیوند میان سه نظریۀ شناختی نامدار شامل دستور شناختی، معناشناسیِ قاب و ساختواژۀ ساخت، چارچوب تحلیلی ژرفنگر و تب…
❤3
🍁 Why do we, like, hesitate when we, um, speak? For as long as we've had language, some people have tried to control it. And some of the most frequent targets of this communication regulation are the ums, ers, and likes that pepper our conversations. These linguistic fillers occur roughly 2 to 3 times per minute in natural speech. So are ums and uhs just a habit we can't break? Or is there more to them? Lorenzo Garc…
❤2👍1
Showing the 12 most recent of 20 posts we hold for @zabanvarzi. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Named by 4 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
Names
Channels on the register whose handles appear in this channel's posts.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
This channel appears in 1 seed channel's Telegram-generated recommendation list in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 18 September 2026 — this entry's latest reading, not the date you are reading this.
“زبانورزی” (@zabanvarzi), 1,964 subscribers as measured 18 September 2026. Telegram Register, tgregister.com/channel/zabanvarzi.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.