2 measurements spanning 2 days. 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 166–168 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
8 Aug 2026, 15:45
167
no change
6 Aug 2026, 19:46
167
first reading
Engagement
20 posts held, back to 1 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 1 pageof Telegram’s post history, 20 posts per page.
ERR · 30 days
67.2%
avg views ÷ 167 subscribers
Avg views / post
112
11 posts measured
Reaction rate
3.73%
reactions ÷ views · ER floor
Posts in window
11
of 20 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 6 August 2026
Posts held
20 (1 July 2026 – 6 August 2026)
Views total
1,234
Reactions total
46
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
8 Aug 2026, 15:45 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
161
Videos
4
Links
105
Lifetime counters from Telegram’s own channel header, read 8 August 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
3m 26s
Average length
1m 43s
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
73 reactions across 19 posts, in 7 distinct kinds. The most used accounts for 72.6% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
53
72.6%
👏
6
8.22%
👌
5
6.85%
🏆
3
4.11%
👍
3
4.11%
🔥
2
2.74%
💯
1
1.37%
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 19 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 73reactions 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 1 July 2026 to 6 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.
📰 نشریه دیتالاین
✨شماره دوم✨
✅ صاحب امتیاز:
انجمن علمی علم اطلاعات و دانششناسی دانشگاه بیرجند
📋 در این شماره خواهید خواند:
🔸متاورس چیست؟ گذری بر جهان موازی و آینده فناوری
🔸تحول در سازماندهی دانش در عصر متاورس
🔸چالشهای اخلاقی و حریم خصوصی در جهان مجازی
🔸کتابخانهها در متاورس؛ تجربهای فراتر از واقعیت
🔸واکاوی پیوند میان علم اطلاعات و اقتصاد دیجیتال در متاورس
🔸روایت یک تجربه ؛ وقتی دانش با واقعیت مجازی گره میخورد
…
📢 تمدید مهلت ثبتنام دومین فراخوان شناسایی و جذب استعدادهای برتر در بخش خصوصی
بنیاد ملی نخبگان مهلت ثبتنام این فراخوان را تا ۲۳ مرداد ۱۴۰۵ تمدید کرده است.
💡 خبر خوب برای دانشآموختگان علم اطلاعات و دانششناسی:
در این فراخوان، امکان ثبتنام برای بسیاری از موقعیتهای شغلی با عنوان علوم کامپیوتر و رشتههای مرتبط نیز وجود دارد. اگر رزومه و مهارت کافی داشته باشید، میتوانید برای این فرصتها اقدام کنید.
🔹 بیش از ۳۰ عنو…
✍️ پروپوزال حرفهای بنویسید؛ هوشمندانه، هدفمند و با کمک هوش مصنوعی! 🤖📚
در کارگاه آنلاین «پروپوزالنویسی با کمک ابزارهای هوش مصنوعی» با تدریس آرزو صبری، از انتخاب و ساختاردهی مسئله تا جستوجوی منابع و استفاده اصولی از ابزارهای هوش مصنوعی را بهصورت کاربردی یاد میگیرید. 🎯
📌 سرفصلهای کارگاه:
🔹 پروپوزال چیست و چه اجزایی دارد؟
🔹 ساختار صحیح بیان مسئله
🔹 آشنایی با سامانههای همانندجو و ایرانداک
🔹 استفاده حرفهای و اخلا…
گاهی بهترین غافلگیریها،
همانهایی هستند که هیچ نشانهای از خودشان باقی نمیگذارند...
ما چیزی برای گفتن داریم؛
اما هنوز نه.
🗓 ۱۵ مرداد
تا آن روز،
حدس بزنید...
https://congress.ilisa.ir/?p=11839
پوستر پژوهشی با عنوان:
«نقش علم باز در ارتقای عدالت اطلاعاتی: مرور نظاممند»
تألیف مبینا بنددار، مهسا ترابی، فاطمهسادات امیری و روحالله خادمی در نهمین کنگره متخصصان علم اطلاعات و دانششناسی پذیرفته شد و در بخش پوستر این همایش ارائه خواهد شد.
این پژوهش به بررسی نقش علم باز در ارتقای عدالت اطلاعاتی و کاهش نابرابریهای دسترسی به دانش از طریق مرور نظاممند ادبیات پرداخته است.
❇️@k…
انجمن علمی علم اطلاعات ودانش شناسی دانشگاه بیرجند تقدیم میکند.
شما دوستان گرامی را دعوت میکنیم
به شنیدن هشتمین قسمت از سری پادکست های انجمن علمی علم اطلاعات و دانش شناسی دانشگاه بیرجند
موضوع این قسمت:
🔸مسیرهای شغلی نو🔸
🎙️گوینده: عطیه خراشادی زاده
✒️متن: عطیه خراشادی زاده
🌌طراحی کاور: زهرا اکبری
-----------------------------------
⭕️ انجمن علمی علم اطلاعات ودانش شناسی دانشگاه بیرجند 🎓
@lisa_Birjand
انجمن علمی علم اطلاعات ودانش شناسی دانشگاه بیرجند تقدیم میکند.
شما دوستان گرامی را دعوت میکنیم
به شنیدن هفتمین قسمت از سری پادکست های انجمن علمی علم اطلاعات و دانش شناسی دانشگاه بیرجند
موضوع این قسمت:
🔸چرا اطلاعات ارزشمندترین دارایی دنیاست🔸
🎙️گوینده: عطیه خراشادی زاده
✒️متن: عطیه خراشادی زاده
🌌طراحی کاور: زهرا اکبری
-----------------------------------
⭕️ انجمن علمی علم اطلاعات ودانش شناسی دانشگاه بیرجند 🎓
@lisa_…
📚 LitReview منتشر شد!
اگر برای نوشتن مرور ادبیات از Scopus یا سایر پایگاهها فایل CSV میگیرید، این ابزار به دردتان میخورد.
فقط فایل CSV شامل عنوان و چکیده مقالات را وارد کنید و به زبان ساده سؤال بپرسید؛ مثلاً:
🔹 مهمترین موضوعات این حوزه چیست؟
🔹 شکافهای پژوهشی کداماند؟
🔹 روند تحقیقات چگونه بوده است؟
LitReview با کمک مدلهای زبانی، پاسخهای تحلیلی و مبتنی بر مقالات موجود در فایل شما تولید میکند.
✅ رابط وب محل…
✍️ پروپوزال حرفهای بنویسید؛ هوشمندانه، هدفمند و با کمک هوش مصنوعی! 🤖📚
در کارگاه آنلاین «پروپوزالنویسی با کمک ابزارهای هوش مصنوعی» با تدریس آرزو صبری، از انتخاب و ساختاردهی مسئله تا جستوجوی منابع و استفاده اصولی از ابزارهای هوش مصنوعی را بهصورت کاربردی یاد میگیرید. 🎯
📌 سرفصلهای کارگاه:
🔹 پروپوزال چیست و چه اجزایی دارد؟
🔹 ساختار صحیح بیان مسئله
🔹 آشنایی با سامانههای همانندجو و ایرانداک
🔹 استفاده حرفهای و اخلا…
🦋گروه علم اطلاعات و دانش شناسی دانشگاه بیرجند تقدیم میکند:
💻InfoShock
🪴اولین لیگ ملی برنامه نویسی در علم اطلاعات در ایران
☘به زودی....
🎥برگزارکننده ها: دانشگاه بیرجند، زیست بوم نوآوری، فناوری و کارآفرینی دانشگاه بیرجند، معاونت پژوهش، فناوری و نوآوری دانشگاه بیرجند، انجمن علمی دانشجویی علم اطلاعات و دانش شناسی دانشگاه بیرجند، شرکت دانش محور مهراد (کتاب بیس)
❇️@ketabbase
✳️@lisa_birjand
انجمن علمی علم اطلاعات ودانش شناسی دانشگاه بیرجند تقدیم میکند.
شما دوستان گرامی را دعوت میکنیم
به شنیدن ششمین قسمت از سری پادکست های انجمن علمی علم اطلاعات و دانش شناسی دانشگاه بیرجند
موضوع این قسمت:
🔸انفجار اطلاعات 🔸
🎙️گوینده: عطیه خراشادی زاده
✒️متن: عطیه خراشادی زاده
🌌طراحی کاور: زهرا اکبری
-----------------------------------
⭕️ انجمن علمی علم اطلاعات ودانش شناسی دانشگاه بیرجند 🎓
@lisa_Birjand
❤3
Showing the 12 most recent of 20 posts we hold for @lisa_Birjand. 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
Citation-graph rank — 27,562 of 1,481,243entries 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.
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 7 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
2
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
2
vacant every time we have ever looked
@lisa_Birjand named 2 handles 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.
@knowledgeandinformationscience named in 1 post, 9 August 2026 – 9 August 2026
@rezaie_843 named in 1 post, 9 August 2026 – 9 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 8 August 2026 — this
entry's latest reading, not the date you are reading this.
“انجمن علمی علم اطلاعات و دانش شناسی دانشگاه بیرجند” (@lisa_Birjand), 167 subscribers as measured 8 August 2026. Telegram Register, tgregister.com/channel/lisa_Birjand.
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.