Channel
@LinkedInLearning_ir
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
28,419subscribers
-31 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
| Telegram ID | -1002085821688 |
|---|---|
| Type | Channel |
| Username | @LinkedInLearning_ir |
| Created | Between 1 November 2023 and 31 May 2024— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 12 August 2026 |
| Measurements held | 7 |
| Confirmed unchanged | 1 time, most recently 12 August 2026 |
| On Telegram | t.me/LinkedInLearning_ir |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 12 Aug 2026, 22:55 | 28,419 | -14 |
| 11 Aug 2026, 21:22 | 28,433 | +3 |
| 10 Aug 2026, 20:41 | 28,430 | -2 |
| 9 Aug 2026, 17:21 | 28,432 | -11 |
| 8 Aug 2026, 17:31 | 28,443 | -7 |
| 7 Aug 2026, 13:30 | 28,450 | no change |
| 7 Aug 2026, 13:15 | 28,450 | first reading |
36 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 16 pagesof Telegram’s post history, 20 posts per page.
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 35 of 36 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 12 August 2026 |
|---|---|
| Posts held | 36 (30 July 2026 – 12 August 2026) |
| Views total | 59,310 |
| Reactions total | 724 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 12 Aug 2026, 21:51 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.
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.
724 reactions across 35 posts, in 10 distinct kinds. The most used accounts for 27.5% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 🤣 | 199 | 27.5% | |
| 👍 | 163 | 22.5% | |
| ❤ | 117 | 16.2% | |
| 😁 | 109 | 15.1% | |
| 👏 | 61 | 8.43% | |
| 😢 | 32 | 4.42% | |
| 👎 | 29 | 4.01% | |
| 😭 | 9 | 1.24% | |
| 🔥 | 4 | 0.552% | |
| 🤯 | 1 | 0.138% |
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 35 of the 36 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 724reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 36 most recent posts we hold, published 30 July 2026 to 12 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.
😝 قوانین جدید الگوریتم لینکدین در ۲۰۲۶ 👨🏻💻 لینکدین دیگه اون لینکدینِ سابق نیست! ترفندهایی که تا همین سال پیش جواب میدادن، دیگه رسماً کارایی ندارن! 🐌 الگوریتم جدید دیگه به تعداد فالوور کاری نداره؛ بلکه مستقیماً به کیفیت، اعتبار و تخصصِ شما پاداش میده. 😊 تو این فایل، نتایج آنالیز ۴۰۰ هزار پست و ۱۱ تغییر اساسی (مثل بازگشت ویدیوها، اینفوگرافیکها و نحوه لینکدهی) رو خلاصه کردم تا استراتژی محتواییت رو سریعاً با ال…
👏7👍1
🫂 دفتر انگلیسی رو این بار میبندی! 🔥 اونم با دوره جامع زبان انگلیسی و ۳ هدیه باارزش که تورو غرق زبان میکنه. 🚀 با ۶۱٪ تخفیف تکرار نشدنی! 💥 به خانواده ۱ میلیون نفری لینگانو بپیوندید! 💯 برای مشاوره و تعیین سطح رایگان، کافیه بهمون پیام بدید 👇👇 @Lingano_support @Lingano_support
♥️ جزوه فارسی «۷ مفهوم ژاپنی که نحوه کار کردنتون رو زیر و رو میکنه!» 👨🏻💻 همیشه برام سوال بود بعضیها چطور تو اوج شلوغی و فشارِ کاری، اینقدر آروم و متمرکزن. جوابش رو تو این ۷ مفهوم باستانی ژاپنی پیدا کردم. ✍🏼 Sina ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 📱 راهنمای تخصصی لینکدین : 👔 @LinkedInLearning_ir
❤8👍2
وبینار دیتا ساینس در دنیای واقعی 🐈⬛📊 از مدلهای یادگیری ماشین تا خلق محصولات دادهمحور 🧬 🧪 💡 فکر میکنید دیتا ساینس فقط ساخت مدلهای پیشبینی است؟ ✅ در واقع، مدلها بهتنهایی ارزشی ایجاد نمیکنند؛ ارزش واقعی زمانی شکل میگیرد که به محصولات دادهمحور (Data Products) تبدیل شوند. 🎯 در این وبینار کوتاه میبینید: 🔴 یک Data Product چیست و چه تفاوتی با مدل Machine Learning دارد؟ 🔴 شرکتها چگونه از #داده برای ساخت محصول…
👍3
👤 جزوه فارسی «بهینهسازی پروفایل لینکدین» 👨🏻💻 بچهها پروفایل لینکدین رزومهتون نیست که عنوانهای شغلی رو توش قطار میکنید! این بزرگترین اشتباهیه که دارین در حق برند شخصیشون میکنید. ✒️ پروفایل لینکدین شما معدن طلای فرصتهای شماست؛ به شرطی که بلد باشی چطور اون متن بیروح رو به یه ارائهی جذاب و حرفهای تبدیل کنی. ✅ من یه «چک لیست ۱۸ قسمتیِ بهینهسازی پروفایل لینکدین» آماده کردم تا باهاش پروفایلتون رو بهینه کنی…
👍1
📊 پروژه را فقط اجرا نکنید؛ آن را هوشمندانه کنترل کنید. اگر میخواهید بدانید پروژه دقیقاً در چه وضعیتی قرار دارد، چقدر از برنامه عقب یا جلو هستید و آیا هزینهها مطابق پیشبینی پیش میروند، یادگیری مدیریت ارزش کسبشده (EVM) یک ضرورت است، نه یک انتخاب. در دوره مدیریت کاربردی ریسک و هزینه (EVM) پروژه با رویکردی کاملاً کاربردی یاد میگیرید: ✔️ عملکرد واقعی پروژه را تحلیل کنید. ✔️ انحراف زمان و هزینه را بهموقع شناسایی ک…
👍1
📓 جزوه فارسی «۱۸ بخش مهم پروفایل لینکدین» 🔃که خیلیا راحت نادیدهاش میگیرن! 👨🏻💻 یه پروفایل قویِ لینکدین، شانسی ساخته نمیشه؛ پشتش یه استراتژی قوی و یه بهینهسازیِ هدفمند وجود داره. ▶️ وقتی تو عمل دیدم تکتک بخشهای پروفایل چه تاثیری تو پرسونال برندینگ دارن و هر کدوم چه هدفی رو دنبال میکنن، تصمیم گرفتم تمام این تجربههام رو تو این جزوه باهاتون به اشتراک بذارم. 📔 تو این فایل رفتم سراغ ۱۸ بخش ضروری در پروفایل ل…
❤8👍1
👨🏻💻 یک روشی رو میخوام بهتون یاد بدم، واسه وقتی که تو لینکدین دارید دنبال کار میگردید و فیلترها رو فعال میکنید. احتمالا لینک مثل این میشه: 🔗 https://www.linkedin.com/jobs/search/?currentJobId=4450661616&f_TPR=r86400&f_WT=2&geoId= 🔴اگه دقت کنید میتونید اون f_TPR=r86400 رو ببینید (بولدش کردم). این یعنی کارهایی که توی ۸۶۴۰۰ ثانیه (۲۴ ساعت) اخیر پست شده باشن برای شما. 🟢اون رو تغییر بدید به r3600 و کارهایی که توی …
🔥4🤯1
👨🏻💻 لینکدین: ✍🏼 mhseyf ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 📱 راهنمای تخصصی لینکدین : 👔 @LinkedInLearning_ir
🤣22❤1😁1
👩🏻💻امروز تو مصاحبه ازم پرسیدن که درچه صورت دیگه با شرکت ادامه نمیدی؟ گفتم بی احترامی. ✍🏼 Farimah ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 📱 راهنمای تخصصی لینکدین : 👔 @LinkedInLearning_ir
👏36😁4🤣3❤2👍2
👩🏻💻 یه مرضی هم هست که رزومه میفرستی و اکسپت هم میشی و بهت زنگم میزنن و قرار مصاحبه هم فیکس میکنی، ولی یهو اوتیسم و افسردگیت میزنه بالا و بعدشم احساس نارسیسیست بودن میکنی و فکر میکنی جاهای بهتری میتونی بری و بعدش کارفرما رو گوست میکنی. ✍🏼 frough ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 📱 راهنمای تخصصی لینکدین : 👔 @LinkedInLearning_ir
😁37😭9❤3👍1
👨🏻💻 واقعا رمز هر موفقیتی استمراره؛ میخوای زبان یاد بگیری؟ استمرار. بدن فیت؟ استمرار. ساز جدید؟ استمرار. رزومه قوی؟ استمرار. مصاحبه موفق؟ استمرار. خلاصه که سمج بودن جواب میده. ✍🏼 mohammad ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 📱 راهنمای تخصصی لینکدین : 👔 @LinkedInLearning_ir
❤19👍6
Showing the 12 most recent of 36 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.
Citation-graph rank — 454,691 of 1,151,006entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Republishes
Channels on the register whose posts this channel has forwarded.
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 2 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.
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 12 August 2026 — this entry's latest reading, not the date you are reading this.
“LinkedIn | لینکدین” (@LinkedInLearning_ir), 28,419 subscribers as measured 12 August 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.