Channel
@bgcgw
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
3,851subscribers
+698 since we began measuring on 9 August 2026
Risers and fallers across the register · movement among entries of 3,162–10,000.
| Telegram ID | -1001608052174 |
|---|---|
| Type | Channel |
| Username | @bgcgw |
| Created | Between 1 December 2021 and 30 April 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 9 August 2026 |
| Last confirmed live | 16 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 16 August 2026 |
| On Telegram | t.me/bgcgw |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 16 Aug 2026, 03:22 | 3,851 | +248 |
| 13 Aug 2026, 00:09 | 3,603 | +431 |
| 9 Aug 2026, 18:12 | 3,172 | +19 |
| 9 Aug 2026, 07:47 | 3,153 | first reading |
5 posts held, back to 8 August 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 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 1 of 5 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 9 August 2026 |
|---|---|
| Posts held | 5 (8 August 2026 – 9 August 2026) |
| Views total | 678 |
| Reactions total | 1 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 9 Aug 2026, 07:47 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 5 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.
1 reaction across 1 post, in 1 kind.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 1 | 100.0% |
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 1 of the 5 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 1reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 5 most recent posts we hold, published 8 August 2026 to 9 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.
复旦大学留学生王水牛疑似卷入多段情感纠纷睡了几百个粉丝不戴套导致怀孕不负责 据透露，王水牛最初以“留学生日常分享”为主题发布视频，迅速吸引上百名女性粉丝追随，部分粉丝在私信中与其建立联系。王水牛在交往中承诺提供情感陪伴却拒绝提供经济支持，甚至在多段关系中导致对方怀孕。 事发后，多名当事人将聊天记录与转账截图曝光，显示其在交往期间多次强调“感情纯净、不谈钱”，却在分手后拒绝承担责任。曝光内容还包括王水牛与不同时间段女性粉丝的亲密合照，以及其在私密群聊中炫耀“零成本恋爱”的言辞。 目前，王水牛本人尚未公开回应，复旦大学方面也未就此事作出官方表态。此事件在留学生圈与抖音社区引发广泛讨论，有人质疑其“学霸人设”与真实行为反差，有人呼吁平台加强监管。 更多视频评论区获取 #王水牛 #约炮 #渣男 #复旦大学
微胖天花板成都肉装杨玉环AI裸舞大尺度三点粉视频曝光 近日，一段以“肉装成都杨玉环”命名的AI生成裸舞视频在多个平台传播，主角原型据称是一位身材微胖、胸围夸张的素人女子，因其样貌与唐代杨贵妃相似而被网友戏称为“成都杨玉环”。据查，该女子此前在短视频平台以古风舞者形象出道，凭借丰满身材与复古妆容积累了近20万粉丝，直播间常以“唐宫贵妃”人设立场，短时间内从普通舞者晋升为平台十佳主播，商业价值一度飙升。 然而，此次AI视频曝光后，其原视频片段被网友反复比对，发现胸部、腰线、步态细节与AI生成内容高度吻合，疑似训练素材直接来源于其付费直播与私密图集。平台随后下架其全部古风视频并封禁账号，品牌合作方也紧急撤稿，事件迅速登上话题热榜。 该女子出道仅一年，从街舞网红转型古风舞姬，再到AI换脸受害者，职业生涯轨迹在不到48小时内崩盘。目前其社交账号已清空，经纪团队尚未回应。但事件本身再次引发公众对AI换脸技术滥用与女性隐私保护的讨论。 …
瓜友的福音来了！发烧友自建的全网吃瓜新闻聚合站正式上线 每天刷电脑手机最爽的事儿，莫过于看到那些精彩绝伦的大瓜、爆料、明星翻车、情感撕逼、社会奇闻……一边嗑瓜子一边看戏，那叫一个过瘾！ 这是一个吃瓜新闻聚合电报，专门为广大吃瓜爱好者服务。借着这股热情，给大家好好介绍一下 为什么要做这个吃瓜聚合站？ 互联网上的瓜实在太多、太散、太乱了：微博热搜刚冒头，抖音就开始短视频轰炸 微信公众号刚出长文分析，B站UP主已经剪辑好视频 小红书、贴吧、论坛到处都有零散爆料…… 你想一次性看全、看懂、看爽？太难了！我们受够了到处跳来跳去的痛苦，于是决定自己动手，打造一个专注吃瓜新闻的聚合平台 我们的吃瓜聚合站有什么特色？ 全网实时聚合，自动抓取微博、微信、头条、抖音、B站、海外平台等各大渠道的热点爆料，第一时间呈现在你面前。错过热搜？在这里补上！ 吃瓜专区，主题清晰 明星塌房/娱乐八卦 网红翻车/情感纠纷 社会奇闻/热点事件 黑料合集/深…
AI乱伦大尺度短剧妹妹的日常到禁忌短剧出道全程揭秘 最近圈子里疯传的AI短剧《妹妹的日常》，女主角那张清纯又带点反差的脸，已经成了不少人的赛博白月光。她不是突然爆红的，而是一步步从私人素材演变成连载系列，出道过程相当有故事。 一切始于创作者对“妹妹”素材的沉迷。他拿着真实生活里的妹妹形象做了大量参考，用AI工具反复生成画面和剧情。一开始只是纯粹的日常向内容：家庭琐事、一起打游戏、大学开学的烦恼、看医生、甚至一些轻松的冒险片段。强度不高，主打温馨陪伴感，创作者自己也说当时“已经玩疯”，但基本还是纯爱路线。 随着制作深入，内容开始出现变化。创作者尝试各种提示词和本地部署方法，逐渐加入IF线（平行人生设定），把妹妹放到不同场景里：恋爱、冒险、更私密的互动。强度明显上升后，他甚至坦言和现实中的妹妹说话时会有点愧疚感。故事线从单纯的家庭日常，慢慢延伸到大学篇、更复杂的情感纠葛，甚至出现了“哥哥把成年妹妹写成黄文投稿到成人话剧社，结果…
❤1
L杯巨乳写真正妹田野优闪电转行，4个月从周刊封面直接杀进S1出道 2003年12月24日出生于神奈川县的田野优（たの ゆう），身高158cm，却拥有110cm的L罩杯、纤细腰身（56cm）和圆脸甜美外貌，被不少人拿来和有村架纯对比，称她是“暗黑有村架纯”或“令和最凶新人”。 她出道的契机，得从小学说起。田野优从小学时代开始就明显感觉到别人总盯着自己的胸部看，与其被别人眼光困扰，不如把这当成自己的武器，做能发挥胸部优势的工作。这份想法一直藏在心里，直到大学时期才正式付诸行动。 2024年1月，她以写真正妹身份登场。《周刊ポスト》2月9·16日合并号把她推上封面，直接冠上“令和史上最高的新人写真偶像”称号。那对夸张却又搭配苗条身材的L杯，瞬间引爆话题。她的X账号开设才3天，就突破1万粉丝。数字写真集也跟着热卖，写真实力迅速被认可。 短短几个月后，剧情急转直下。2024年5月，《周刊ポスト》5月7日号正式公布她将转职为性感女优…
Showing the 5 most recent of 5 posts we hold for @bgcgw. 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 — 832,410 of 1,481,217entries 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.
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 1 registered channel — 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.
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 16 August 2026 — this entry's latest reading, not the date you are reading this.
“八卦吃瓜网” (@bgcgw), 3,851 subscribers as measured 16 August 2026. Telegram Register, tgregister.com/channel/bgcgw.
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.