Telegram customer service anti-harassment guide: identifying spam messages, malicious users and risk control strategies

Telegram Bot customer service is a powerful assistant for cross-border teams and community operators, but it is also naturally exposed to the harassment risks of the open ecosystem: spam ads, malicious screen spam, false inquiries… If no precautions are taken, customer service efficiency will drop sharply, and the experience of real users will also be dragged down.

This article provides a set of implementable Telegram customer service anti-harassment strategies from identification, defense to automated risk control. Whether you are a novice team that has just launched Bot or an operator with a certain number of users, you can find a suitable solution.

Why does Telegram Bot customer service need an anti-harassment mechanism?

Telegram’s Bot API is designed to be open and flexible: any user can send messages directly to the Bot without adding friends. This brings convenience, but also means that the bot’s inbox is naturally exposed to all Telegram users - including spammers, marketing bots, and malicious users.

Consequences of not dealing with harassment:

• Customer service efficiency decreases: Agents need to spend a lot of time screening real inquiries, and labor costs increase.
• Real user churn: When users see a large number of advertising messages while queuing, they will think that the Bot lacks management, reducing their trust.
• Data Pollution: A large amount of invalid data is mixed into user portraits and statistics, leading to deviations in operational decision-making.

Therefore, anti-harassment is not an “optional enhancement” but rather an infrastructure for bot customer service operations.

Three common types of harassment scenarios and identification methods

Spam ads and phishing links

Identification Features:

• The message content contains short links (such as t.me/xxx, bit.ly/xxx) and is not related to the product.
• Repeated words: The same sentence is sent by multiple different users in a short period of time.
• Promoting non-related products: For example, promoting cryptocurrency and adult content in your customer service bot.

Identification method:

• Set up a keyword blacklist (such as “get it for free”, “click to register”, “limited time benefit”).
• Perform “review before display” processing for new user messages containing links.

Malicious screen spam and abuse

Identification Features:

• A single user sends more than 5 messages in a row within 10 seconds.
• The same message is sent more than 3 times.
• Attacking questions: using insulting words and threatening language.

Identification method:

• Monitor message frequency: trigger temporary traffic limit for high-frequency users.
• Create a sensitive vocabulary library (including insulting words and threatening phrases) and automatically mark or ignore it.

Repeated registration and false inquiries

Identification Features:

• Multiple accounts use similar phrases (such as “Hello, how can we cooperate?”), but the accounts have no avatars and the registration time is short.
• Users do not pay attention to the reply content and only continue to send private message links.
• The account sends the same message synchronously among multiple Bots (can be detected through ID behavior pattern).

Identification method:

• Check user Telegram account age: newly registered accounts (if less than 7 days old) are marked as “to be observed”.
• Check whether the user has interacted with other Bots: Accounts with no historical behavior will enter the verification process first.

Basic defense strategy: set keyword filtering and message frequency limit

No complicated system is required, you can immediately implement the following two measures in the Bot backend:

Keyword blacklist

Establish a list of core keywords and directly ignore or automatically reply to preset prompts (such as “Your message contains sensitive content, please resend”) to new user messages containing these words.

Recommended initial keywords: 免费, 注册链接, 点击, bit.ly, t.me/joinchat, 限时, 送钱.

Message frequency limit

Set a maximum messaging frequency for an individual user. Recommended initial value: Up to 3 messages from the same user within 10 seconds; if the limit is exceeded, the current limit will be triggered - the user’s messages will enter the isolation queue, and the agent must manually confirm before they can see it.

Intermediate defense strategy: user portrait analysis and hierarchical processing

When the number of Bot users exceeds 1,000, basic filtering is often not enough - harassers will use variant keywords and send them at random intervals. At this time, user portraits need to be introduced to classify users.

Establish user trust level system

Level	Definition	Typical Behavior	Handling
Trust	Active user, no violations in history	Multiple consultations, valid conversation records	Normal display, priority allocation of seats
Normal	New user or low-frequency user	First consultation, no abnormal behavior	Normal display, but the message enters the normal queue
Suspicious	Trigger keywords or frequency thresholds	Contains black-named words and is sent frequently	The message enters the isolation queue and is manually reviewed by the agent
Blacklist	Confirmed violations or repeated harassment	Triggered quarantine multiple times, marked by agents	Ignore directly and will not be displayed in the conversation list

Operating steps:

1. Label each user (trusted/suspicious/blacklisted) in the customer service background.
2. For messages from suspicious users, the agent will not be notified by default and will only be kept in the “pending review” list.
3. Messages from blacklisted users are directly ignored and do not occupy agent resources.

Example of automated processing rules

Assuming you use TG-Staff’s visual command process editor, you can configure the rules like this:

• Rule 1: The user sends a message → Check whether the message contains blacklist keywords → Yes → Set the user label to “suspicious” → The message jumps to the quarantine queue → The agent will review it and decide whether to move it to a normal conversation.
• Rule 2: The user sends more than 3 messages within 10 seconds → automatically sends the “You are sending too frequently, please try again later” prompt → the user label is set to “Suspicious” → the flow is limited within 30 minutes (only 1 message is allowed every 30 seconds).
• Rule 3: The user is marked as a “spammer” by the agent → the user label is set to “blacklist” → all subsequent messages will be ignored and will not be displayed in the conversation list.

Advanced defense strategy: use tools to achieve automated risk control

When teams manage multiple bot projects simultaneously and receive hundreds of messages per day, the cost of manually handling each suspicious message becomes unacceptable. At this time, you need to use the customer service SaaS platform to achieve automated risk control.

TG-Staff provides two functions that are very suitable for this scenario:

• Visual command process editor: Build risk control rules with zero code, such as the “suspicious user → quarantine queue → agent review” process mentioned above, which can be completed by dragging and dropping without writing a line of code.
• User Portraits and Statistics (Professional Edition): Automatically records each user’s account age, message frequency, historical behavior, and label changes, helping you quickly identify typical harassment patterns of “new registration, no avatar, high-frequency sending”.

Example:

1. Create a “risk control process” in TG-Staff: The user sends a message → Check the user profile (the account is less than 7 days old and has no profile photo) → Yes → Set the user label to “suspicious” → The message enters the quarantine queue → Agent review.
2. Cooperate with the automatic translation function: If the spam message is sent in a non-Chinese language, AI will automatically detect the keywords after translation and also trigger isolation.

If you are using Telegram Bot for cross-border customer service, Telegram customer service anti-harassment is not a one-time configuration, but a continuous optimization process. Tools can help you reduce duplication of effort, but strategies need to be constantly adjusted as harassment patterns change.

Banning and Ignoring: When should you take action and when should you tolerate it?

A question that many operators struggle with: when encountering a suspicious user, should they be banned directly or should they be observed first? Wrong judgment may result in accidental injury to real customers.

Best Practices: Graded Punishment

Penalty level	Trigger conditions	Specific operations
Warning	Trigger frequency limit or keyword for the first time	Automatically reply to prompts, no ban
Current limit	Trigger again within 30 minutes	Limit message frequency (such as 1 message every 30 seconds) for 1 hour
Temporary ban	Triggered multiple times within 24 hours	Block user for 24 hours, message will not be displayed
Permanently banned	Confirmed malicious spamming/advertising/attacks	User added to blacklist, all messages ignored

The key step: Give the user a “verification” opportunity

Before triggering a temporary ban, it is recommended that users complete a simple verification to avoid accidentally injuring real customers. For example:

Your message contains sensitive content. If you are a real user, please reply “I am a real person” to remove the restriction.

If the user replies with the correct keyword, the restriction will be automatically lifted and restored to the “normal” level; if the user does not reply or repeats the violation, it will be upgraded to a temporary ban.

Anti-harassment checklist (10 items)

Copy the following list and verify it on each bot you manage:

• Message frequency limit is turned on (recommended: up to 3 messages from the same user within 10 seconds)
• A keyword blacklist has been established (at least 15 core words)
• A quarantine queue has been set up for suspicious users
• Established user trust level system (trust/normal/suspicious/blacklist)
• The “verification” process before banning has been configured (such as replying to specific keywords)
• Hierarchical punishment rules have been set (warning→current limiting→temporary ban→permanent ban)
• User portrait recording has been enabled (account age, message frequency, historical behavior)
• Complaint channels have been set (such as customer service Bot or email)
• A weekly review of risk control rules has been scheduled
• The message records in the quarantine queue have been backed up (for subsequent analysis of harassment patterns)

Start implementing the anti-harassment strategy today

Preventing harassment is not a once-and-for-all task, but a daily routine for Bot customer service operations. Starting with basic filtering and gradually introducing user profiling and automated risk control, your team can defend the line of defense before harassment escalates.

If you want to spend less time configuring these rules, you can try TG-Staff’s visual command process editor - you can drag and drop to build a risk control process without development. The professional version also provides user portraits and statistics to help you quickly identify harassment patterns.

ACT NOW:

• Go to TG-Staff official website to sign up for a 3-day free trial
• Refer to Command Process and User Statistics Document for detailed configuration
• Contact customer service Bot @tgstaff_robot for configuration help

Your bot deserves a clean inbox.