24 measurements spanning 29 days, net +15. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 13,681–13,717 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 24
Measured (UTC)
Subscribers
Change
3 Sept 2026, 15:57
13,710
+4
2 Sept 2026, 07:26
13,706
+2
1 Sept 2026, 05:25
13,704
-1
31 Aug 2026, 08:42
13,705
-4
30 Aug 2026, 05:33
13,709
+1
29 Aug 2026, 03:56
13,708
-2
27 Aug 2026, 03:47
13,710
-1
25 Aug 2026, 05:27
13,711
-2
24 Aug 2026, 03:25
13,713
+19
22 Aug 2026, 12:15
13,694
+8
20 Aug 2026, 23:37
13,686
-1
19 Aug 2026, 20:58
13,687
-1
17 Aug 2026, 18:28
13,688
-2
16 Aug 2026, 17:44
13,690
+1
13 Aug 2026, 18:26
13,689
-1
12 Aug 2026, 19:37
13,690
-1
11 Aug 2026, 21:07
13,691
-1
10 Aug 2026, 18:07
13,692
-7
9 Aug 2026, 17:01
13,699
+4
8 Aug 2026, 14:26
13,695
first reading
Engagement
31 posts held, back to 19 February 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 50 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
16.4%
avg views ÷ 13,710 subscribers
Avg views / post
2,250
11 posts measured
Reaction rate
1.22%
reactions ÷ views · ER floor
Posts in window
11
of 31 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
What these figures were computed from
Window
Rolling 30 days · latest post in window 1 September 2026
Posts held
31 (19 February 2026 – 1 September 2026)
Views total
24,780
Reactions total
302
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
2 Sept 2026, 19:48 UTC
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
Video runtime
10m 49s
Average length
10m 49s
Measured directly from 1 video 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.
Reaction mix
1,161 reactions across 29 posts, in 2 distinct kinds. The most used accounts for 85.9% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
👍
997
85.9%
👎
164
14.1%
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 31 of the 31 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 1,198 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 31 most recent posts we hold, published 19 February 2026 to 1 September 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.
نظریهای در دفاع از فهم مردم
#A 488
✍️ آرمان امیری @armanparian - زمستان سال ۹۶ دختری در خیابان انقلاب بر روی یک صندوق برق رفت. روسری سفیدش را سر چوب زد و در سکوت ایستاد. تصویر و خبر این حرکت اعتراضی ساده مثل یک بمب خبری منفجر شد. یکی از نمادینترین اعتراضات در تاریخ حجاب اجباری، آن هم از جانب کسی که هیچکس حتی نام او را نمیدانست. نه یک فعال سیاسی بود، نه یک کنشگر حقوق بشر، نه یک فمنیست شناخته شده. مدتی پس از بازد…
✍️ آرمان امیری @armanparian - مجموعه یادداشتهای «تبارشناسی فساد ساختاری در لایههای اجتماعی ایران» در سه قسمت منتشر شد. همانطور که پیشتر اشاره کرده بودم، این سه یادداشت، باب انتشار در فضای مجازی تا حدودی خلاصهسازی شده بودند و فاقد ارجاعات علمی، یا مثالها و شواهد جزئی بودند. اینجا نسخهی کامل این مجموعه را به صورت یک فایل پیدیاف منتشر میکنم. در این فایل، علاوه بر متن سه یادداشت پیشین، توضیحات تکمیلی هم اضافه شد…
از مجموعهی #نظریه_تجدد_ایرانی
#تبارشناسی فساد ساختاری در لایههای اجتماعی ایران
بخش ۳: جمهوری اسلامی
اتحاد علیه دولت!
#A 487
✍️ آرمان امیری @armanparian - در دههی هفتاد خورشیدی، یکی از اصطلاحات بسیار پرکاربرد در ادبیات سیاسی غیررسمی که نقل محافل بود و جوکهای فراوانی هم با آن ساخته میشد تعبیر «آقازاده» بود. در فرهنگ عامه و عمومی، آقازاده، مترادف قشری از زالوهای اقتصادی بود که به مدد نسبت فامیلی با مقامات دول…
✍️ بخش سوم از مجموعهی سه قسمتی «تبارشناسی فساد ساختاری در لایههای اجتماعی ایران»
بخش نخست: «پیدایش زالوهای مکنده»
بخش دوم: «تراژدی نوسازی»
بخش سوم: اتحاد علیه دولت!
کانال «مجمع دیوانگان»
@DivaneSara
.
از مجموعهی #نظریه_تجدد_ایرانی
#تبارشناسی فساد ساختاری در لایههای اجتماعی ایران
بخش ۲: پهلوی
تراژدی نوسازی
#A 486
عصمت باقرپور در بابل به دنیا آمد؛ به تاریخ سوم اسفند ۱۳۰۳، در خانوادهای فرودست با ۱۰ فرزند. امورات خانواده پیش از مرگ پدر هم به سختی میگذشت. پدر که مرد عصمت تازه دوازده ساله شده بود و سواد خواندن و نوشتن هم نداشت. یک بقچهی کوچک بست، سوار کامیون رانندهای شد که از دوستان پدرش بود و به سمت تهران به…
#A 486
✍️ بخش دوم از مجموعهی سه قسمتی «تبارشناسی فساد ساختاری در لایههای اجتماعی ایران»
بخش نخست با عنوان «پیدایش زالوهای مکنده» را از اینجا بخوانید.
کانال «مجمع دیوانگان»
@DivaneSara
.
از مجموعهی #نظریه_تجدد_ایرانی
#تبارشناسی فساد ساختاری در لایههای اجتماعی ایران
بخش ۱: صفویه و قاجار
پیدایش زالوهای مکنده
#A 485
✍️ آرمان امیری @armanparian - هاینریش بروگش، خاورشناس و دیپلمات ارشد دولت فخیمهی پروس بود؛ در سال ۱۸۶۰ در قالب هیأتی دیپلماتیک به ایران سفر کرد و به دربار ناصرالدینشاه جوان رفت. او بعدها در سفرنامهی خود نوشت:
«شاه به وزیری نیاز داشت تا خشم تودهی مردم را [از ساحت شاه] منحرف کند، …
#A 485
✍️ این یادداشت، بخش نخست از مجموعهی سه قسمتی «تبارشناسی فساد ساختاری در لایههای اجتماعی ایران» است. هدف نهایی این مجموعه، یافتن ربشههای یک عفونت ساختاری در لایهبندی جامعهی ایرانی است که به نظر میرسد چندین قرن سابقه دارد، بر ناکارآمدی و فساددولتهای ایرانی اثر میگذارد و در برابر جنبشهای اجتماعی ایران به مقاومت بر میخیزد. این مجموعه، به شکل یک طرح نظریهی جدید ارائه میشود که میتواند برای تکمیل و اصلا…
فلسفیدن بر بستر شاهنامه
#A 484
✍️ آرمان امیری @armanparian - حتما شنیدهاید که میگویند «زبان فارسی زبان مناسبی برای فلسفه نیست». مدعیان گاه به برخی اصطلاحات فلسفی اشاره میکنند که معادل فارسی مناسبی ندارند؛ مترجمان یا برای بازگرداندن آنها به فارسی به مشکل میخورند، یا اساسا قید این کار را میزنند و از خود واژهی اصلی استفاده میکنند. (مثلا «دازاین» یا «سوژه»)
به گمان من، چنین ادعایی محصول وارونه گرفتن جای علت و …
✍️ آرمان امیری @armanparian - موضوع این گفتار، بازخوانی دلایل و پیامدهای تغییر در تاریخنگاری دوران مشروطه، به ویژه از جانب ناسیونالیستهای قرن ۱۳ خورشیدی است.
پرسش این است که آیا گرایش ناسیونالیستهای نخستین به تاریخنگاری ملی و سرهنویسی پارسی، تنها یک واکنش احساسی و رومانتیک به عقبماندگی کشور بود؟
آیا آنها بی هیچ توجهی به سنت و تاریخ کشور، مشغول جدالی متعصبانه و شبهفاشیستی با سنت و فرهنگ جامعه بودند؟ (آنچنان …
👍28👎1
Showing the 12 most recent of 31 posts we hold for @divanesara. 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.
Forward network
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.
Mentions
Named by 8 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.
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.
Handles this channel named that no longer answer
Dead references
1
handles named in this channel’s posts, vacant today
Evidenced gone
0
we ourselves saw one of these resolve, at some point
Never seen alive
1
vacant every time we have ever looked
@divanesara named 1 handle that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all. A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
Never seen alive
References a handle that is not a live channel — we have no record it ever was one.
@divanesara_ named in 19 posts, 8 August 2026 – 21 August 2026
Cite this entry
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 3 September 2026 — this
entry's latest reading, not the date you are reading this.
“مجمع دیوانگان” (@divanesara), 13,710 subscribers as measured 3 September 2026. Telegram Register, tgregister.com/channel/divanesara.
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.