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Fix typo and add link to the post in multiple languages
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content/en/post/2025/a_telegram_spam_blocker_bot_based_on_bayesian.md

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title = "A Telegram Spam Blocker Bot Based On Bayesian Algorithm"
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date = 2025-08-30T10:34:00-07:00
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lastmod = 2025-09-01T10:06:38-07:00
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tags = ["telegram", "bot", "design"]
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lastmod = 2025-09-01T21:36:06-07:00
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tags = ["telegram", "design", "programming", "rails", "rust"]
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categories = ["design"]
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draft = false
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toc = true
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[中文版本](https://ramsayleung.github.io/zh/post/2025/%E4%B8%80%E4%B8%AA%E8%87%AA%E5%AD%A6%E4%B9%A0%E7%9A%84telegram%E5%B9%BF%E5%91%8A%E6%8B%A6%E6%88%AA%E6%9C%BA%E5%99%A8%E4%BA%BA/)
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## <span class="section-num">1</span> Preface {#preface}
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I spent a weekend building a Telegram spam blocker bot based on Bayesian Algorithm. The project is open-sourced at: <https://github.com/ramsayleung/bayes_spam_sniper>
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I spent a weekend building a Telegram spam blocker bot based on Bayesian Algorithm `@BayesSpamSniperBot` (<https://t.me/BayesSpamSniperBot>). The project is open-sourced at: <https://github.com/ramsayleung/bayes_spam_sniper>
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### <span class="section-num">1.1</span> Telegram {#telegram}
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I usually listen to podcasts while running and cooking. 《[软件那些事儿(A podcast in Chinese about history and story behind software)](https://podcasts.apple.com/us/podcast/%E8%BD%AF%E4%BB%B6%E9%82%A3%E4%BA%9B%E4%BA%8B%E5%84%BF/id1147186605)[^fn:1] is one of my favorites, hosted by [栋哥](https://liuyandong.com/sample-page)&nbsp;[^fn:2]. Because I enjoyed 栋哥's show, I took the chance to join his Telegram channel.
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栋哥's Telegram channel [汗牛充栋](https://t.me/huruanhuying)&nbsp;[^fn:3] is primarily used for releasing podcast information.
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He once opened the comment function, but it unexpectedly attracted a flood of crypto-related users posting spam, leading him to disable comments:
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He once enabled the comment for channel, but it unexpectedly attracted a flood of crypto-related users posting spam, leading him to disable comments:
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{{< figure src="/ox-hugo/spam_concert.jpg" >}}
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content/zh/post/2025/一个自学习的Telegram广告拦截机器人.md

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title = "一个自学习的Telegram广告拦截机器人"
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date = 2025-08-28T23:45:00-07:00
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lastmod = 2025-09-01T14:29:58-07:00
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lastmod = 2025-09-01T21:35:34-07:00
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tags = ["telegram", "design", "programming", "rails", "rust"]
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categories = ["telegram"]
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draft = false
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[English Version](https://ramsayleung.github.io/en/post/2025/a_telegram_spam_blocker_bot_based_on_bayesian/)
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## <span class="section-num">1</span> 序言 {#序言}
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我花了一周末时间,写了一个自学习的 Telegram 广告拦截机器人 `@BayesSpamSniperBot` (<https://t.me/BayesSpamSniperBot>),项目开源在:<https://github.com/ramsayleung/bayes_spam_sniper>
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### <span class="section-num">3.1</span> 贝叶斯定理 {#贝叶斯定理}
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提起概率算法,最经典的例子莫过于「抛硬币」这一古典概型——每次抛掷都是独立事件,前一次的结果不会影响下一次的概率。
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提起概率算法,最经典的例子莫过于「抛硬币」这一古典概率——每次抛掷都是独立事件,前一次的结果不会影响下一次的概率。
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然而,现实中的很多场景并不能像抛硬币那样无限重复,事件之间也往往并非相互独立。
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