Channel
@bg99599
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry
97,353subscribers
-2,652 since we began measuring on 10 August 2026
Risers and fallers across the register · movement among entries of 31,623–100,000.
| Telegram ID | -1001987339623 |
|---|---|
| Type | Channel |
| Username | @bg99599 |
| Description | #头条 #曝光 #吃瓜 #缅北 #博彩 #新闻发布 #行业新闻 #博彩热点 #曝光骗子 #柬埔寨 #菲律宾 #泰国 #缅甸 本频道专注分享东南亚华人大小事，揭秘灰产内幕，记录真实故事，足不出户让你看尽东南亚！ 商务/投稿: @yinger0122 划水摸鱼讨论组 ： @dny8886 |
| Created | Between 1 March 2023 and 31 October 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 10 August 2026 |
| Last confirmed live | 13 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 13 August 2026 |
| On Telegram | t.me/bg99599 |
National news — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 10 August 2026 and assigned it the closest of 31 fixed categories, at 52% 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.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 13 Aug 2026, 07:15 | 97,353 | -727 |
| 12 Aug 2026, 04:53 | 98,080 | -1,594 |
| 11 Aug 2026, 03:45 | 99,674 | -331 |
| 10 Aug 2026, 13:45 | 100,005 | first reading |
47 posts held, back to 9 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 6 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 24 of 43 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 | 47 (9 August 2026 – 12 August 2026) |
| Views total | 458,033 |
| Reactions total | 31 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 13 Aug 2026, 07:52 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.
Lifetime counters from Telegram’s own channel header, read 13 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
Measured directly from 10 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.
31 reactions across 23 posts, in 9 distinct kinds. The most used accounts for 22.6% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 😁 | 7 | 22.6% | |
| 🔥 | 6 | 19.4% | |
| ❤ | 5 | 16.1% | |
| 🤩 | 4 | 12.9% | |
| 👍 | 3 | 9.68% | |
| 🎉 | 2 | 6.45% | |
| 👏 | 2 | 6.45% | |
| ⚡ | 1 | 3.23% | |
| 🥰 | 1 | 3.23% |
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 24 of the 47 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 31reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 47 most recent posts we hold, published 9 August 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.
#频道互推 #相似推荐 ------------------------------ 📢 东南亚吃瓜🍉 📢 东南亚大事件｜华人新闻｜热点爆料 📢 东南亚大事件 「新闻曝光」 📢 西安艺妓 📢 Telegram-环球新闻时讯 📢 全球新闻聚焦 | 📣国际热点速报 📢 【新事件】东南亚曝光l新闻l吃瓜🅥 📢 全球新闻事件 📢 东南亚大事件 - 新闻头条 📢 美丽东南亚-新闻-曝光🎀 ------------------------------ ☁️ 互推入口：@sooo
SOSO 搜搜 把 Telegram 搜索和发现放在一个入口。 想继续找频道、群组、帖子或贴纸，直接输入关键词。 也可以从发现页继续浏览同类入口。
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#群友投稿 此人中文名：鄭雅 英文名： Diana 為菲律賓OKADA,SOLAIRE實體數字貴賓廳CEO, 此人打一份工領了4-5份薪水‼️‼️‼️ 聯合自家市場部偷吃客人B數，如果客人贏錢利用自己職務之便讓帳房同事竄改數據，造成公司利益損失。 後面新來的菲籍同事轉到仲介去，實際上根本也沒看過真的仲介，利用此手法偷賺背後大股東錢。 假設名義要替菲籍員工保社保，實則把錢放自己口袋 公司任何外籍簽證與公司某位股東小舅子合作，收費比外面任何仲介還要貴，效率又慢，把自家員工當殺豬盤 員工辦工作簽證期間，因個人因素可能需要提離職，說要賠付23萬，過了幾個月後員工自己花錢找仲介查辦工作簽證進度根本就沒送進去辦，如當下賠付這筆錢又是被公司吃掉了 利用自己職務要求自家主管到競爭對手的貴賓廳幫忙培訓它家新員工，自己也加入了競爭對手的貴賓廳工作群組 下次就是把護照丟給中國公部門
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#产业新闻 数千 #非洲人 如何进入 #妙瓦底 诈骗园区的？❓❓ 近年来，位于泰缅边境地区的诈骗产业持续受到国际关注。其中，一个长期存在的疑问逐渐浮出水面：大量来自非洲多个国家的人员，是如何跨越近万公里，最终进入缅甸妙瓦底一带的诈骗园区？ 据当地相关信息显示，妙瓦底以及泰国来兴府湄索县、朴帕县沿莫艾河一带的部分诈骗窝点内，曾出现大量非洲国家人员身影。这些人员来源涉及多个国家，其中部分人声称是受到高薪工作招聘吸引，自愿前往；也有人表示遭遇虚假招聘、欺骗甚至人口贩卖，最终被困诈骗园区。 据了解，部分人员的跨境路线十分复杂。他们可能从非洲多个国家出发，经航空交通抵达东南亚，再进入泰国境内，随后前往泰缅边境地区，最终通过非正规渠道进入缅甸妙瓦底相关区域。 这一过程中，多个环节引发外界关注。 首先，在入境环节，大量人员需要办理泰国签证并留下相关资料。如果背后存在中介或组织协助办理，那么签证申请材料、代理机构以及相关人员是否能够…
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#产业新闻 #巴基斯坦 狗推被端后续🔥🔥 昨天中午 12 通知说今天必须把所有人都放出来，下午6点又来了一波警察还有一个大巴车，又开始查，说 7 点就能出来，到现在也没一个人出去。 大楼法人是马来西亚华人，也被控制在楼里面。目前大楼里面最少有500-1000名狗推。 ps:大厂被端了呀🫶
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#马媒报道 #马来西亚 警方捣毁 #入室盗窃团伙 5个 #中国小偷 被捕🆘🆘 马来西亚吉隆坡警方近日展开两次突击行动，逮捕5名涉嫌组成入室盗窃团伙的中国籍男子，并查获车辆、作案工具、被盗物品以及超过1万林吉特现金。 据马来西亚《新海峡时报》8月12日报道，案件源于8月4日发生在吉隆坡Taman Desa Petaling的一起住宅盗窃案。受害者报案称，家中现金、珠宝以及一个价值约6000林吉特的名牌斜挎包被盗。 吉隆坡警方接获线报并展开调查后，于8月9日晚9时30分，分别在Sri Petaling和Pudu展开突击行动，成功逮捕5名男子。吉隆坡警方负责人表示，行动中查获一辆车辆，以及多种疑似用于破门入屋的工具，同时找回部分属于上述盗窃案受害者的物品。 警方还起获多件其他贵重物品，包括手表和钱包等，以及超过1万林吉特现金，其中包括多种外币。 初步调查显示，这5名嫌疑人年龄介于38岁至58岁，警方怀疑他们除了涉嫌参与Ta…
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#柬媒报道 #木牌 发生绑架案！3名 #越南绑匪 落网⚠️ 昨天上午，柬埔寨柴桢省巴域市发生一起绑架案。接到民众报警后，警方迅速展开追捕。 面对围追堵截，3名嫌犯在途中弃置一名越南籍受害人，并弃车逃入稻田。警方包围搜查，于当天上午10时30分成功抓获3名越南籍男嫌犯，现场扣押作案车辆、3把刀具及手机等物品，受害人安全获救。 目前，案件正依法移交地方法院审理。 ps:绑匪死全家🔫
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#柬媒报道 #柬埔寨 10天内驱逐788名非法外国人，涉及21个国家和地区，315名中国籍涉诈人员被遣返⚠️ 柬埔寨移民总局近日公布，在8月1日至8月10日期间，柬埔寨执法部门依法驱逐非法居留及涉嫌违法犯罪的外国人共788人，其中女性92人，涉及21个国家和地区。 被驱逐人员涉及的21个国家和地区包括：韩国、中国、中国台湾、多哥、泰国、尼日利亚、尼泊尔、孟加拉国、巴基斯坦、马来西亚、缅甸、利比里亚、越南、新加坡、斯里兰卡、加纳、印度、印度尼西亚、意大利、德国以及埃及。 其中：315名中国籍人员（包括15名女性）因涉嫌参与网络诈骗（Online Scams）犯罪活动，被柬埔寨通过特别航班遣返回中国，分别经由德崇国际机场和暹粒吴哥国际机场出境。 14名泰国籍人员（包括3名女性）因涉及网络诈骗案件，被通过戈公省柴桢国际边境口岸（Cham Yeam International Border Gate）遣返回泰国。 此外，其余…
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#产业新闻 中国24岁女子 #泰国 失联 疑被拐卖至缅甸电诈园区⚠️ 拥有3000万粉丝的网红“陈泽”爆料，中国24岁女子祁萌与网友前往东南亚旅游后失联。 据悉，祁萌于8月2日自吉隆坡飞抵曼谷，在登上声称开往清迈的车辆后便失去联系，疑遭转卖至缅甸妙瓦底电诈园区。 消息在网络引发广泛关注，祁萌好友在网上发布寻人启事并希望联系绑匪协商赎金。 PS：泰国不能去呀🤬
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#产业新闻 #太子集团 调查再升级：美国文件曝光“2号共谋者”，被指暗中保护诈骗网络🔥 8月10日，据泰媒援引美国案件文件及OCCRP调查报道，柬埔寨太子集团（Prince Group）涉嫌涉及跨国网络诈骗、数字资产投资诈骗、洗钱及人口贩运等活动。 报道将一名被美国案件文件称为“2号共谋者（Co-Conspirator 2）”的人士指向陈索利（Chen Sokly）。据称，他曾在太子集团相关企业担任风险控制领域的管理职务，并被指负责跟踪警方调查情况、与执法部门进行协调，甚至试图通过各种方式保护涉嫌诈骗的相关场所及资金网络。 报道称，柬埔寨官方资料显示，一名原名陈星（Chen Xing）的人士于2017年获得柬埔寨国籍，并使用“陈索利”这一姓名，这与调查报告所披露的身份信息存在对应关系。 美国方面此前已将太子集团及相关人员列入制裁范围，并指其网络涉及利用被贩运人员实施网络诈骗，包括所谓“杀猪盘”式数字货币投资骗局。美国司…
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#老挝新闻 #老挝万象 突袭电诈窝点，261人被控制！其中153名中国人‼️‼️‼️ 老挝公安部8月8日出动特别行动人员，对首都万象塔銮湖经济特区内一处涉嫌电信网络诈骗、呼叫中心诈骗及网络赌博的场所展开突击清查，现场共控制261人，其中包括153名中国人。 此次行动地点位于万象市赛色塔区蓬坦村，涉事场所设在老挝塔銮投资发展有限公司相关建筑内。 警方通报显示，现场261人来自7个国家和地区，其中女性35人。具体包括： 中国籍153人，其中女性1人 越南籍61人，其中女性16人 泰国籍36人，其中女性16人 台湾地区5人，其中女性1人 马来西亚籍2人 韩国籍2人 缅甸籍2人，其中女性1人 行动中，警方还查获大量用于网络活动的设备，包括404台台式电脑、86台笔记本电脑和2355部手机。 老挝警方称，该场所涉嫌同时从事电信网络诈骗、呼叫中心诈骗和网络赌博等违法活动。经过现场调查，警方确认相关建筑属于老挝塔銮投资发展有限公司…
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#产业新闻 #巴基斯坦 一窝狗推被端‼️‼️ #中国大使馆 联合当地警方排查了位于Gulberg green的一栋办公大楼，据信扫描了在场所有人的护照并采集了生物信息！ 目前已羁押在现场等待结果。目的是排查这些人的违法行为，涉案人员将会遣送回国处理。 ps: 巴铁不铁了🤣
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Showing the 12 most recent of 47 posts we hold for @bg99599. 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.
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.
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
This channel appears in 7 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
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 13 August 2026 — this entry's latest reading, not the date you are reading this.
“东南亚新闻博彩曝光” (@bg99599), 97,353 subscribers as measured 13 August 2026. Telegram Register, tgregister.com/channel/bg99599.
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.