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Telegram profile photo for LinkedIn | لینکدین

Channel

LinkedIn | لینکدین

@LinkedInLearning_ir

On this record: Topic · Observations · Channels linked by matching posts · Growth · Engagement · What this channel posts · Reactions · Posts · Posts edited after publishing · Posts recorded as deleted · Citations · Handles named that no longer answer · Cite this entry

29,702subscribers

+1,252 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 10,000–31,623.

Register entry

Telegram ID-1002085821688
TypeChannel
Username@LinkedInLearning_ir
Description👔 راهنمای تخصصی لینکدین 📊 جهت درج تبلیغات، به آیدی زیر پیام دهید: 👇🏼 🆔 @IEMAds
CreatedBetween 1 November 2023 and 31 May 2024 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live9 November 2026
Measurements held62
Confirmed unchanged2 times, most recently 9 November 2026
On Telegramt.me/LinkedInLearning_ir

Topic

Job listings — 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 10 September 2026 and assigned it the closest of 31 fixed categories, at 81% 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 the other topics.

Observations

These are measurements, not verdicts. Each one below states something we counted, alongside the evidence it was counted from, so you can check it rather than take it. None of them is graded: every observation this register holds is recorded at severity 0, because the precision of the detectors behind them has not been measured yet, and a rating we cannot support is worse than none. Read each as a fact about the data, not as a judgement about the channel. How we measure.

Content that also appears on other registered channels

Posts published here appear in near-verbatim form on 1 other registered channel. This entry also sits inside a group of 3 channels linked by matching posts, directly or through other channels in the group; two members of the group may share no posts with each other. The matching is on the text itself, not on Telegram’s forward marker, so it finds a copy whether or not it was labelled as one.

Matching posts — open both and compare (5 of the pairs behind the counts below)
Posted firstThenOverlapGap
@LinkedInLearning_ir/2379 · this entry16 Aug 2026, 15:26 UTC@IEMTwitter/897916 Aug 2026, 15:26 UTC1.00under a minute
@IEMTwitter/902124 Aug 2026, 20:28 UTC@LinkedInLearning_ir/2392 · this entry24 Aug 2026, 20:28 UTC1.00under a minute
@LinkedInLearning_ir/2394 · this entry25 Aug 2026, 07:30 UTC@IEMTwitter/902325 Aug 2026, 07:30 UTC1.00under a minute
@LinkedInLearning_ir/2400 · this entry26 Aug 2026, 16:27 UTC@IEMTwitter/902926 Aug 2026, 16:27 UTC1.00under a minute
@IEMTwitter/912525 Oct 2026, 09:30 UTC@LinkedInLearning_ir/2470 · this entry25 Oct 2026, 09:30 UTC1.00under a minute
Channels this entry shares post bodies with, where at least one matching post pair has no forward credit between the two channels, or to a common source, recorded in our data
ChannelMatching postsText overlapTypical gapPublished first
@IEMTwitter20 (8/8 hand-verifiable sample passed)1.00under a minute@IEMTwitter (17–3)

Text overlap is the Jaccard coefficient over the set of distinct three-word phrases in the two bodies: 1.00 is identical wording, and the threshold for counting a pair at all is 0.70. Candidates are generated by simhash LSH (4 x 16-bit bands, exact Hamming <= 3) verified against the bodies with Jaccard over the SET of distinct 3-word shingles. Published first counts which side of each matching pair carries the earlier timestamp — in this corpus, which is the limitation directly below.

What this cannot establish

MEASURED, DOMINANT ERROR SOURCE: a post ingested before 2026-08-06 may have carried a forward header that was not recorded. A 45-pair hand-check against live t.me pages found 14 (31%) where the live page shows a forward header naming the other channel and the database has none, plus 4 more (9%) naming a third party. The text match itself was wrong 0 times out of 45. Read attribution_capture.items_in_trusted_window before treating the unattributed count as a claim.

Telegram lets a channel forward a post with a header naming the source, and we only began reliably recording that header on 2026-08-06. 11 of the 11 matches recorded here fall after that date and carried no header when we read them. The rest predate reliable capture and are not evidence either way.

“Published first” means first in this corpus. We hold 132 comparable posts for this entry, running 30 July 2026 to 10 November 2026. A channel we have read one page deep will look younger than a neighbour we have read in full, and the order would flip with no change in the underlying facts.

The detector’s own notes on this observation, as it recorded them. Names in this_style are fields of the underlying evidence record, which the plain-English paragraphs above read out for this entry.

  • Verbatim republication has three causes and the text separates only two: a clone/mirror, unattributed copy-paste, or BOTH channels copying a common third source that neither attributes. The spread filter (content held by at most 8 channels) reduces the third and does not remove it.
  • 'Earliest' means earliest IN THIS CORPUS. A channel ingested one page deep will look younger than a neighbour ingested in full; corpus_coverage above is there to be checked before the direction is believed.
  • shared_verified_est extrapolates the sampled pass rate over the full narrow match count; sampled/passed are the numbers actually measured.
  • Absence of a forward header is not proof of intent: Telegram lets a channel disable forward attribution, and a credit written in the body is not parsed as attribution here (mention_edge_either_way above is the closest available signal).

Of the group of 3 channels, the one whose matching posts we hold earliest is @LinkedInLearning_ir — which is this entry. That is a statement about our reading window, not a claim of authorship.

Recorded under the key clone_copy, last confirmed 11 November 2026. An observation that a later pass no longer finds is cleared, and a cleared observation is removed from this page rather than being shown struck through — we do not keep publishing a claim we have withdrawn. Dispute an observation.

Channels linked by matching posts

This channel is in a group of 3 registered channels linked by matching posts, directly or through other channels in the group. This channel may share no posts with some of the channels listed below, and listed channels may share none with each other. Links are found by comparing text fingerprints across every channel on the register.

Which channel, if either, published first is deliberately not shown in this list. “First” could only mean first in our own crawl, which depends on how deep we happened to reach into each channel’s history, not on when the content was actually first posted. Our one hand-check read 45 of the highest-confidence flagged pairs against the live Telegram pages. In 18 of them the live page showed a “Forwarded from” credit that our database had no record of, so reading that pair as an unattributed copy was wrong; the check did not measure who published first. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Near-verbatim matching has several ordinary explanations besides copying — a channel mirroring itself, an unattributed repost, or two channels independently repeating the same wire story — and this measurement cannot tell those apart. How this is measured.

Growth

28,34529,87029,107.529,7027 August 20268 November 2026
62 measurements spanning 93 days, net +1,252. 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 28,116–30,099 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 62
Measured (UTC)SubscribersChange
8 Nov 2026, 08:1629,702-7
7 Nov 2026, 00:2629,709-4
6 Nov 2026, 01:0029,713-6
4 Nov 2026, 23:3529,719+40
3 Nov 2026, 23:0729,679-7
3 Nov 2026, 01:1929,686-21
2 Nov 2026, 00:5429,707-3
1 Nov 2026, 03:0829,710-4
31 Oct 2026, 04:5629,714-8
30 Oct 2026, 06:0829,722-6
29 Oct 2026, 04:0129,728-9
27 Oct 2026, 23:1429,737-13
26 Oct 2026, 21:3129,750-4
25 Oct 2026, 21:2729,754-5
24 Oct 2026, 20:1829,759-1
23 Oct 2026, 19:5129,760-10
22 Oct 2026, 18:0129,770-5
21 Oct 2026, 11:3629,775-7
20 Oct 2026, 06:3029,782-9
19 Oct 2026, 05:4029,791first reading

Engagement

134 posts held, back to 30 July 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 159 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
7.41%
avg views ÷ 29,702 subscribers
Avg views / post
2,200
42 posts measured
Reaction rate
0.187%
reactions ÷ views · ER floor
Posts in window
42
of 134 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. It is computed over the 40 of 42 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 10 November 2026
Posts held134 (30 July 2026 – 10 November 2026)
Views total92,394
Reactions total170
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken11 Nov 2026, 00:11 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

Photos
563
Videos
216
Links
403

Lifetime counters from Telegram’s own channel header, read 11 November 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.

Video runtime
4m 15s
Average length
2m 08s

Measured directly from 2 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.

Reaction mix

1,280 reactions across 124 posts, in 14 distinct kinds. The most used accounts for 36.6% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
❤46936.6%
👍27821.7%
🤣21316.6%
😁1179.14%
👏796.17%
👎524.06%
😢322.50%
🔥141.09%
😭110.859%
👌50.391%
🤡50.391%
😍30.234%
👨‍💻10.078%
🤯10.078%

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 127 of the 134 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 1,280 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 134 most recent posts we hold, published 30 July 2026 to 10 November 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.

Recent posts

Telegram’s own ‘edited’ marker: 2 of 42 posts held from the last 30 days (4.76%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).

10 Nov 2026, 20:03 UTC377 viewsread 11 November 2026
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🐍 مسیر کار و کسب درآمد در هوش مصنوعی رو از پایتون شروع کن! 😀 دوره جامع پایتون آموزشگاه تحلیل داده ⚡️از صفر، پروژه‌محور و قدم‌به‌قدم همراه مدرس. 🏫 ۲۷ سال تجربه آموزشی و امکان مراجعه حضوری 💻 کلاس حضوری و آنلاین هم‌زمان 🛠 رفع اشکال واقعی روی کد خودت؛ حضوری یا با AnyDesk 🎥 فیلم جلسات و گروه پشتیبانی فعال بعد از دوره ✅ آموزشگاه دارای مجوز رسمی؛ مدرک مورد تأیید فنی‌وحرفه‌ای 💳 پرداخت اقساطی | تخفیف پرداخت نقدی 👇 زمان کل…

10 Nov 2026, 16:35 UTC727 views2 reactionsread 11 November 2026
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😝 ۹ الگوی طلایی تولید محتوا در لینکدین 👩🏻‍💻 وقتی همه پست‌هات یه شکل باشن، خیلی زود تکراری می‌شن؛ چه برای مخاطب، چه برای خودت. تنوع در نوع محتوا کمک می‌کنه موضوعاتت رو از زاویه‌های مختلف مطرح کنی. 🩺 هر مدل پست یه کاربرد متفاوت داره؛ یکی برای آموزش خوبه، یکی برای دیده شدن تجربه شخصی، یکی برای شروع بحث و تعامل و یکی هم برای نشون دادن نتیجه و اعتبار کاری. 📄 در فایل زیر، به معرفی ۹ مدل پست لینکدین پرداختم؛ از پست‌ها…

❤2

10 Nov 2026, 14:25 UTC793 views1 reactionsread 11 November 2026
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🔥 وبینار رایگان «#تفکر_داده_محور در تصمیم‌های منابع انسانی» 📊🚀 🎯چطور می‌توان با استفاده از داده‌ها، تصمیم‌های هوشمندانه‌تری در منابع انسانی گرفت و از مدیریت سنتی به مدیریت داده‌محور رسید؟  🎙 سخنران: مهندس خسروی 🔹 مدیر سابق منابع انسانی دیجی‌کالا 💡 در این وبینار: 🔹 آشنایی با مفهوم و ضرورت منابع انسانی داده‌محور 🔹 بررسی تاریخچه و سیر تحول People Analytics 🔹 شناخت داده‌ها و شاخص‌های کلیدی منابع انسانی 🔹 بررسی نقش هوش …

👍1

9 Nov 2026, 17:29 UTC≈1,300 views6 reactionsread 11 November 2026
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👨‍💻 ۹ وبسایت برای پیدا کردن فرصت‌های شغلی ریموت 👩🏻‍💻 برای پیدا کردن پوزیشن‌های ریموت، خودت رو به لینکدین و Upwork محدود نکن! ⬅️ لینکدین پلتفرم فوق‌العاده‌ایه، اما رقابت تو نقش‌های ریموتِ اون وحشتناکه. از طرفی Upwork هم بیشتر برای پروژه‌های فریلنسری مناسبه تا موقعیت‌های فول‌تایم. 😀 چندین جاب‌برد تخصصی وجود دارن که تمرکزشون مستقیماً روی موقعیت‌های ریموته و پیدا کردن فرصت‌های مرتبط رو راحت‌تر می‌کنن. 1⃣ وب‌سایت W…

👍4❤2

9 Nov 2026, 16:27 UTC≈1,180 views3 reactionsread 11 November 2026
Forwarded from @hashtagadsPhoto

بورسیه‌ی ۲۰۰۰ دلاری دانشگاه هاروارد با مکتب‌خونه 🎉 مکتب‌خونه به مناسبت تولد ۱۵ سالگیش یک قرعه‌کشی ویژه برگزار کرده؛ از بورسیه دوره آموزشی دانشگاه هاروارد به ارزش ۲۰۰۰ دلار گرفته تا اشتراک ChatGPT Plus و مکتب‌خونه داره جایزه میده. 🔥 علاوه بر جوایز قرعه‌کشی، تخفیف‌های ویژه تولد مکتب‌خونه هم فعال شده: ▪️ ۸۰٪ تخفیف اشتراک یک‌ساله ▪️ تا ۷۰٪ تخفیف دوره‌های آموزشی ⏳ مهلت: تا ۲۲ آبان 🔗 برای مشاهده جوایز و شرایط شرکت در…

❤3

8 Nov 2026, 17:40 UTC≈1,590 views4 reactionsread 11 November 2026
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👨🏻‍💻 این پرامپت رو بده ChatGPT و بهش رزومه و جاب دیسکریپشنِ موقعیت موردنظرت رو بده. مدل خودش نقش یک مدیر ارشد استخدام رو می‌گیره و بررسی می‌کنه برای اون موقعیت چه مهارت‌ها، تجربه‌ها، پروژه‌ها و ویژگی‌هایی اهمیت بیشتری دارن. ⭕️ چیزی که خروجی رو کاربردی می‌کنه اینه که؛ فقط نمی‌گه «رزومه خوبه یا بد»؛ بلکه ازت می‌خواد مشخص کنه چه چیزهایی باعث می‌شن یک کاندیدا متمایز بشه، چه مهارت‌های فنی و نرمی مهمن، چه تجربه‌هایی ارزش…

❤2👎1🔥1

8 Nov 2026, 15:35 UTC≈1,490 views2 reactionsread 11 November 2026
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📚 می‌خوای یه مهارت واقعی یاد بگیری که برات درآمد بسازه؟ قدم اولش رایگانه! 😍 لازم نیست از قبل چیزی بلد باشی. توی یه وبینار کاملا رایگان می‌بینی دقیقاً از کجا شروع می‌شه و قدم‌به‌قدم چطور جلو می‌ری 🎁 آموزش کامل، اتصال مستقیم کارجو و کارفرما و امکان پرداخت اقساطی، همه توی یه پلتفرم 🤝 اگه دنبال یه مهارت و درآمد کنار درس و دانشگاه هستی، این فرصت رو از دست نده! 👇 ثبت‌نام در وبینار رایگان ثبت‌نام در وبینار رایگان ثبت‌ن…

❤2

8 Nov 2026, 14:28 UTC≈1,300 views2 reactionsread 10 November 2026
Forwarded from @BabakMohammadpour_irPhoto

⚙️ رشد بدون استخدام: ۵ کار تکراری که می‌شود خودکارش کرد قبل از استخدام نیروی جدید، این کارها را خودکار کنید: ۱. جواب سؤال‌های تکراری مشتری: پاسخ آماده یا ربات ساده ۲. یادآوری پرداخت: پیامک زمان‌بندی‌شده ۳. ثبت سفارش: فرم آنلاین متصل به جدول ۴. گزارش فروش روزانه: داشبورد ساده به‌جای جمع‌زدن دستی ۵. پیش‌نویس متن‌ها: با هوش مصنوعی، و فقط ویرایش 💡 از پرزحمت‌ترین کار شروع کنید. ❓ کدام کار تکراری بیشترین وقت شما را می‌…

👍1👎1

8 Nov 2026, 13:26 UTC≈1,440 views2 reactionsread 10 November 2026
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🚀 نقشه راه ورود به بازار فریلنسینگ بین‌المللی طراح، برنامه‌نویس، دیزاینر یا تولیدکننده محتوا هستی؟ اما هنوز نمی‌دانی چطور با همین مهارتی که داری، پروژه بین‌المللی بگیری و درآمد دلاری داشته باشی؟ در این وبینار، قدم‌به‌قدم بررسی می‌کنیم که یک متخصص ایرانی چطور می‌تواند پروژه دلاری بگیرد و مسیر فریلنسینگ بین‌المللی را شروع کند. 🎙 مدرس: حسین جوان کیش 📅 دوشنبه 18 آبان ⏰ ساعت ۱۹ این وبینار مناسب شماست اگر: ▪️ اگر فریلن…

🤣2

4 Nov 2026, 16:03 UTC≈2,600 views3 reactionsread 11 November 2026
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📄 ۲۱ نکته برای افزایش شانس استخدام 🤩 چک‌لیست عملی جست‌وجوی کار 👨🏻‍💻 جست‌وجوی شغلی فقط این نیست که یه رزومه بفرستی و منتظر بمونی؛ باید بدونی کجا، چه زمانی و با چه روشی درخواست بدی و چطور بعد از اپلای و مصاحبه، ارتباطت رو ادامه بدی. 📝 در فایل بالا، ۲۱ نکته کاربردی برای بهتر کردن فرایند استخدام آوردم؛ از زمان‌بندی اپلای و شخصی‌سازی رزومه و لینکدین گرفته تا شبکه‌سازی، ارتباط مستقیم با شرکت‌ها، آماده شدن برای مصاحبه، …

❤1👍1🤡1

4 Nov 2026, 14:24 UTC≈1,250 views1 reactionsread 7 November 2026
Forwarded from @hashtagadsPhoto

🔵 دوره آموزشی هوش مصنوعی در برندسازی 🚀 برندتان را با ترکیب خلاقیت انسانی و هوش مصنوعی، هوشمندتر و متمایزتر بسازید! در این دوره با کاربرد هوش مصنوعی در استراتژی برند، طراحی هویت بصری و تولید محتوای هوشمند آشنا می‌شوید. 👥 مناسب: مدیران کسب‌وکار، بازاریابان، طراحان و.. 👨🏻‍🏫 مدرسان: المیرا فاتح و مجتبی حسن‌پور 📅 شروع: ۲۶ آبان 💻 برگزاری: آنلاین | ۲ جلسه ۴ ساعته 🏛 گواهینامه قابل ترجمه از سازمان جهاد دانشگاهی صنعتی شر…

👍1

3 Nov 2026, 15:45 UTC≈3,340 views5 reactionsread 11 November 2026
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🤖 ویرایش حرفه‌ای رزومه با کلاد ⬅️ در کمتر از ۵ دقیقه 👨🏻‍💻 باگِ اصلیِ رزومه شما کمبود سابقه کار نیست؛ مشکل، نحوه‌ی پرزنت کردنِ تجربه‌ها، بخش خلاصه و چیدمان مهارت‌هاست که باعث می‌شه ریکروتر تو بررسی اولیه رزومه‌تون، شما رو ریجکت کنه. 📃 با این ۶ پرامپت می‌تونی بخش‌های مختلف رزومه‌ات رو طوری بهینه کنی که انگار یه کوچِ حرفه‌ایِ روش کار کرده. 1️⃣ اول ببین رزومه‌ات واقعاً چه ایرادهایی داره: 💬 این رزومه فعلی منه: [رز…

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Showing the 12 most recent of 134 posts we hold for @LinkedInLearning_ir. 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.

Posts edited after publishing

@LinkedInLearning_ir edited 2 posts after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.

An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.

First edit seen
22 August 2026
Most recent edit
3 November 2026

Posts recorded as deleted after publishing

We recorded 8 posts on @LinkedInLearning_ir as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.

A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.

First seen missing
13 August 2026
Most recent seen missing
9 October 2026
Median time before seen missing
12 days
Seen missing in the last 30 days
0

Forward network

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 6 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.

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.

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 8 November 2026 — this entry's latest reading, not the date you are reading this.

“LinkedIn | لینکدین” (@LinkedInLearning_ir), 29,702 subscribers as measured 8 November 2026. Telegram Register, tgregister.com/channel/LinkedInLearning_ir.

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.