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Channel

西港大事件|西港日记|西港新闻

@xigang315

On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Stars · Posts · Citations · Cite this entry

36,573subscribers

+5,247 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 31,623–100,000.

Register entry

Telegram ID-1002663195876
TypeChannel
Username@xigang315
CreatedBetween 1 March 2025 and 31 July 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live15 September 2026
Measurements held32
Confirmed unchanged1 time, most recently 15 September 2026
On Telegramt.me/xigang315

Topic

Gambling & betting — 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 9 September 2026 and assigned it the closest of 31 fixed categories, at 76% 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 why it has no browse page of its own yet.

Growth

31,32636,77234,0497 August 2026 — 31,326 subscribers7 August 2026 — 31,326 subscribers8 August 2026 — 31,411 subscribers9 August 2026 — 31,584 subscribers10 August 2026 — 31,769 subscribers11 August 2026 — 31,812 subscribers12 August 2026 — 31,928 subscribers14 August 2026 — 32,044 subscribers15 August 2026 — 32,471 subscribers16 August 2026 — 32,615 subscribers17 August 2026 — 32,812 subscribers19 August 2026 — 32,859 subscribers20 August 2026 — 33,097 subscribers21 August 2026 — 33,294 subscribers22 August 2026 — 33,616 subscribers24 August 2026 — 33,952 subscribers25 August 2026 — 34,141 subscribers26 August 2026 — 34,319 subscribers27 August 2026 — 34,555 subscribers27 August 2026 — 34,789 subscribers28 August 2026 — 34,891 subscribers29 August 2026 — 35,103 subscribers30 August 2026 — 35,391 subscribers1 September 2026 — 36,571 subscribers1 September 2026 — 36,772 subscribers3 September 2026 — 36,748 subscribers4 September 2026 — 36,734 subscribers7 September 2026 — 36,710 subscribers10 September 2026 — 36,671 subscribers12 September 2026 — 36,625 subscribers14 September 2026 — 36,592 subscribers15 September 2026 — 36,573 subscribers36,5737 August 202615 September 2026
32 measurements spanning 39 days, net +5,247. 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 30,509–37,589 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 32
Measured (UTC)SubscribersChange
15 Sept 2026, 23:1936,573-19
14 Sept 2026, 09:4036,592-33
12 Sept 2026, 15:5736,625-46
10 Sept 2026, 11:4036,671-39
7 Sept 2026, 06:5736,710-24
4 Sept 2026, 14:2136,734-14
3 Sept 2026, 01:5336,748-24
1 Sept 2026, 23:4636,772+201
1 Sept 2026, 00:5536,571+1,180
30 Aug 2026, 23:3835,391+288
29 Aug 2026, 20:3435,103+212
28 Aug 2026, 21:1634,891+102
27 Aug 2026, 21:1734,789+234
27 Aug 2026, 00:0834,555+236
26 Aug 2026, 02:5234,319+178
25 Aug 2026, 06:2434,141+189
24 Aug 2026, 09:3433,952+336
22 Aug 2026, 14:3333,616+322
21 Aug 2026, 04:5733,294+197
20 Aug 2026, 04:2333,097first reading

Engagement

830 posts held, back to 7 August 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 70 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
3.10%
avg views ÷ 36,573 subscribers
Avg views / post
1,130
563 posts measured
Reaction rate
0.082%
reactions ÷ views · ER floor
Posts in window
563
of 830 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 190 of 563 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 September 2026
Posts held830 (7 August 202610 September 2026)
Views total638,186
Reactions total183
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken10 Sept 2026, 14:13 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
3h 00m
Average length
41s

Measured directly from 262 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

256 reactions across 202 posts, in 13 distinct kinds. The most used accounts for 73.4% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
18873.4%
👍135.08%
🎉83.13%
👏83.13%
😁83.13%
🥰83.13%
🤩72.73%
🔥62.34%
51.95%
💩20.781%
👌10.391%
👎10.391%
🤔10.391%

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 262 of the 830 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 256 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 830 most recent posts we hold, published 7 August 2026 to 10 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.

Telegram Stars

Stars received
1
across the posts below
Posts paid on
1
of 830 we hold a reading for · 0.1%
Most on one post
1
single highest reading

A paid reaction is a reader spending Telegram Stars — bought with money — on a post by @xigang315. Telegram publishes the count on the public post preview alongside ordinary reactions, and this register reads it there. It is the only figure on this site that measures money moving rather than attention.

Stars are not reactions, and the two are never added. They are rendered in the same strip on Telegram and counted in the same shape, but one is a tap and the other is a purchase. The reaction totals and the engagement rate elsewhere on this page exclude every figure in this section, and no rate here is computed against a reaction count.

This is not revenue, and we publish no currency figure. What a Star costs a reader and what it pays a channel are different numbers, Telegram takes a share we cannot observe, and the terms have changed. Converting a Star count into money would be an estimate dressed as a measurement, so the count is where we stop.

Counted over the 830 most recent posts we hold for this entry, published 7 August 2026 to 10 September 2026. Star counts above 1,000 reach us in Telegram’s short form and carry the same three-significant-figure rounding as everything else on this page.

Recent posts

10 Sept 2026, 14:00 UTC756 viewsread 10 September 2026
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10 Sept 2026, 13:51 UTC829 views1 reactionsread 10 September 2026
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KNA每日从水沟谷向觉凯图运送中国籍电诈人员 据当地居民消息,克伦民族军(KNA)每日从妙瓦底以北的水沟谷,将大量中国籍电诈人员运送至克伦民族联盟(KNU)第7旅辖区帕安县觉凯图地区。 KNA以高价租用村民车辆,并派兵随行安保。自2025年起,觉凯图山谷已建起大量电诈建筑并投入运营。KNU法昂县称已阻止施工,但该地点距觉凯图很近。KNA早在2023年便在当地大量购地。 目前,觉凯图山谷多处已建成大型建筑,靠近泰国湄索对岸湄拉村。社交媒体以“觉凯图与湄拉中国城”为名招聘电诈人员及厨师、清洁、安保等。 据悉,电诈人员除中国籍外,还有他国外籍人员及缅甸本地应聘者。此前KK园区、敏拉班等地电诈园区遭军方作秀式捣毁后,从业者已扩散至克伦邦多地及萨尔温江以西继续运营。 📣 西港大事件: @xigang315 👉 投稿: @SN777

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10 Sept 2026, 13:30 UTC881 viewsread 10 September 2026
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10 Sept 2026, 13:16 UTC943 viewsread 10 September 2026
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拜林省要求8家赌场月底前停止网络博彩 拜林省省长潘斯雷莫女士今天下午召开会议,向位于拜林省萨拉克劳县斯登卡奇乡的8家赌场负责人下达命令,要求这些赌场必须在9月底前停止所有网络博彩活动。 潘斯雷莫表示,打击和清除各种形式的非法赌博,尤其是通过互联网进行的网络博彩,是政府目前重点整治的工作之一,因为这类活动会严重影响社会治安、社会秩序以及民众的生活。 她明确规定,9月30日是最后期限。从10月1日起,当局将直接对这些赌场进行检查,并依法采取行动,不会给予任何例外或宽限。 同时,拜林省所有赌场今后只允许接待亲自到赌场现场赌博的客人,不得开展网络博彩业务。 潘斯雷莫还要求地方政府和各级执法部门严格执行政府命令,持续跟进和调查相关情况,确保9月底之后,拜林省成为一个没有网络博彩活动的示范省份。 📣 西港大事件: @xigang315 👉 投稿: @SN777

10 Sept 2026, 13:00 UTC973 viewsread 10 September 2026
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10 Sept 2026, 12:46 UTC≈1,020 views1 reactionsread 10 September 2026
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#网友投稿:西港星沙湾公寓物业通知明天查电脑设备 📣 西港大事件: @xigang315 👉 投稿: @SN777

1

10 Sept 2026, 12:30 UTC≈1,040 viewsread 10 September 2026
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10 Sept 2026, 12:16 UTC≈1,060 views1 reactionsread 10 September 2026
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#群友投稿:东南亚乱不乱,还是得看中国人说了算。但凡有消费能力的,大多也都是中国人。 普遍来讲,只要你会说中文,不管是当地本地人,还是其他国家的外籍人员,在东南亚这一片区域,工资收入普遍都会比不会说中文的高出不少。 当地很多岗位招聘的时候,都会把会中文作为硬性要求。就拿柬埔寨金边来说,不少场所里面,一群外籍人员,还专门去学习中文歌,专门唱给中国客人听。 在东南亚很多地方,中文已经变成一项很吃香的能力。掌握中文,就意味着能获得更多工作机会,拿到更高的薪资待遇。 📣 西港大事件: @xigang315 👉 投稿: @SN777

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10 Sept 2026, 12:00 UTC≈1,060 viewsread 10 September 2026
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10 Sept 2026, 11:42 UTC≈1,090 views1 reactionsread 10 September 2026
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供桌变“欢场”!泰著名皇家寺庙住持卷走9200万公款,与女信徒僧舍犯戒视频曝光 泰国佛教界爆发震撼全国的严重丑闻!大城府(Ayutthaya)千年皇家古刹佛泰沙旺寺(Wat Phutthaisawan)66岁知名住持“龙婆绰”(Luang Por Chot),因涉嫌盗用高达9200万泰铢(约合数千万元人民币)的寺庙捐款并转移给亲密女信徒,于今日清晨被警方逮捕。 令人震惊的是,警方在调取其僧舍监控时,意外搜出大量极其露骨的犯戒画面——该名住持竟直接在信徒日常摆放供品与斋饭的供桌上,与该名女信徒公然发生性行为! 今日(10日)上午6时许,泰国中央调查局(CIB)联合打击犯罪局(CSD)出动大批警力突击搜查该寺庙。警方初步调查显示,该住持涉嫌通过私自开具20多张虚假寺庙收据,将本属于寺庙的9200万泰铢捐款,频繁转入其亲密女资助人的个人账户,涉嫌严重职务侵占与洗钱罪。 然而,搜查过程中最令人发指的“炸裂”内幕随之浮出水面。执法

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10 Sept 2026, 11:13 UTC≈1,140 views0 reactionsread 10 September 2026
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#澄清柬埔寨大部分赌场没有使用黑色电子牌靴 首先不可否认黑盒子的确是可以作弊杀猪,控制发庄家想要的牌。 但是柬埔寨很多赌场用的都是百十块钱的透明塑料牌靴,这种牌靴发牌过程大家都看得到,想靠什么黑科技在里面作弊,哪有那么容易?当然,也不排除行业里确实存在很多输不起的赌场,偷偷使用几十万的 #黑盒子 。 至于有人说荷官可以靠什么手法、手速作弊,那麻烦你先看看柬埔寨赌场发牌的都是什么人。基本都是本地柬埔寨人,要不是我们这些人来这里投资开赌场,他们很多人可能还在村里种田。一个个平时连标准的赌场流程都要培训很久,你觉得他们能有什么电影里面那种神乎其神的手法和手速? 所以不要什么事情都往“荷官靠手法换牌”上面扯。真要讨论赌场有没有作弊,就看设备、流程和监管,而不是整天幻想每个发牌员都是赌神级别的高手。 而且柬埔寨很多赌场使用的都是透明塑料牌靴,整个发牌过程是可以直接看到的。甚至可以在客人下注之前,现场先发出6张牌,让客人亲眼确认牌

10 Sept 2026, 10:54 UTC≈1,170 views3 reactionsread 10 September 2026
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报告:🇺🇦乌克兰电诈产业月营业额或达10亿美元 涉6万人从业 打击跨国有组织犯罪全球倡议(GI-TOC)报告显示,乌克兰境内约有1000至1500个电诈呼叫中心,雇佣约6万人,月营业额高达10亿美元,受害者覆盖全球29国。 该网络交织黑帮、毒贩及执法机关。近期,乌总检察长办公室官员克拉皮瓦因涉嫌充当电诈“保护伞”被捕;受此腐败案牵连,乌总检察长克拉夫琴科已于周一晚宣布辞职。目前乌方正与爱沙尼亚等国联合跨境查封相关窝点。 是不是忙于打仗 所以管控更松? 📣 西港大事件: @xigang315 👉 投稿: @SN777

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Showing the 12 most recent of 830 posts we hold for @xigang315. 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.

Mentions

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 15 September 2026 — this entry's latest reading, not the date you are reading this.

“西港大事件|西港日记|西港新闻” (@xigang315), 36,573 subscribers as measured 15 September 2026. Telegram Register, tgregister.com/channel/xigang315.

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.