Analysis of Batch Telegram Username Detection Method: Improving Screen Number Efficiency and Accuracy

existIn Telegram marketing, user management and automated reach scenarios, teams often need to handle thousands of Telegram usernames (@username). These usernames come from different channels: advertising collection, form registration, third-party data, old user export, etc. But do these usernames actually exist? Has your account been banned? Can we communicate? Many people are not sure.

If the team directly imports these user names into the system to perform batch operations (adding friends, sending messages, joining groups, and pushing notifications) without any filtering, the final result is often:

lThe operation failure rate is high and the execution process is frequently interrupted;

lMessage delivery is not in place and the value of data is diluted;

lThe account triggers risk control and the platform restricts actions frequently;

lThe mass sending efficiency is low and the conversion rate decreases;

lWasted costs and poor project results.

Therefore, in anyBefore batch operations of Telegram usernames, you need to complete the "username screening" action—— That is rightTelegram usernames are structured to detect, filter out abnormal users, and retain communicable, authentic, and valid accounts.

This article will focus on this demand and dismantle the batchTelegram username detection operation method, tool selection, data processing process, and combinationdigital planetThe platform’s professional capabilities in this field provide a set of efficient and practical execution models.

 

1. WhyTelegram username filtering needs to be "batch processed"?

Many people will ask: Can I check each one manually? Of course you can, but when you are faced with thousands, tens of thousands, or even hundreds of thousands of user name data,Manual inspection efficiency is extremely low, the error rate is extremely high, and the cost is very uneconomical..

Common questions include:

lDoes this username exist?

lHas your account been banned?

lCan I receive messages from strangers?

l whether it isBot account?

lHave you been inactive for a long time?

lDoes frequent blocking of others lead to risk control?

If these problems are not clearly identified in batches, any subsequent delivery behavior will be based on Being on the "wrong list" will ultimately only lower the overall ROI of the project.

Therefore, using a set of professional tools to detect and label in batches is a standard action that data-driven teams cannot skip.

 

2. CommonTelegram username exception type

During the actual screening process,"Abnormal" situations in Telegram usernames mainly include:

1. Username does not exist

Due to a typo, fake registration, or data sync issue, the username never existedTelegram does not exist and is the most direct invalid target.

2. Being banned

Telegram has strict restrictions on account abuse (such as group chatting, mass messaging, and fraud). Once banned, the username will be retained but cannot be used.

3. Unable to receive messages from strangers

Some users have turned off communication permissions for non-friends, resulting in messages that cannot be delivered or are automatically blocked by the system even if they are sent.

4. Non-individual users (Bot accounts)

Accounts used for automated scripts or docking robots do not have regular user behavior value.

5. Accounts that have been inactive for a long time

ExceedUsers who have not been online or active for 30 days have a very low probability of responding and will contribute little to conversions.

6. Reported risk accounts

There are behaviors such as being blocked and reported by multiple users, and the platform determines that For "highly sensitive" targets, exposure to such accounts can easily trigger your own risk control.

These abnormal accounts will become the main source of failures, misjudgments, and complaints in batch operations, and must be cleared during the screening process.

 

3. Overview of the batch inspection capabilities of the Digital Planet platform

In order to cope with large-scaleTelegram username detection requirements,Digital Planet Platform provides specializedTelegram detection module supports users to import batch data, in-depth status recognition, automatic labeling and structured export.

The detection dimensions supported by the platform include:

lDoes the username exist?

l Is the user active (recently7 days/last 30 days)

lIs the account banned?

l Is itBot account

lWhether the information is complete (avatar, signature)

lWhether it can be reached by messages from non-contacts

lAre there high-frequency account ban records or complaint labels?

These detection results will be annotated in a structured format after each user name, helping the team filter, classify and prioritize the list.

 

4. Standard operating procedures for batch testing

To help the team complete quicklyTelegram username cleaning, the following is a set of practical detection process suggestions:

Step 1: Prepare username data

All usernames to be detected (@username) are organized into one column, and it is recommended to save it as a CSV or TXT file. The format should be unified. It is recommended that all entries be prefixed with "@" to avoid mixing Chinese and English or interfering with spaces.

Step 2: Import into the digital planet detection module

After logging into the platform, go toUpload files in the Telegram module, and the system automatically identifies and removes common problems such as format errors and duplicate data.

Step 3: Select detection items

You can choose detection content based on business scenarios, such as only detecting "Existence", or adding dimensions such as "activity + communication + account ban identification".

Step 4: Start batch testing

The system will automatically assign tasks and perform detection. The processing capability supports concurrent detection of 10,000-level data, which can be completed on a regular scale in dozens of minutes.

Step 5: View the detection results and mark the classification

After the detection is completed, all user names will be marked as follows Labels such as “Already Existing”, “Account Banned”, “Unable to Communicate”, “High Activity” etc.

Step 6: Filter and export available usernames

You can freely filter the user group export for required tag combinations, such as "Exists + Active + Non-Bot + Communicable" is used for subsequent delivery or script operations.

 

5. How to use test results to improve execution results?

The results after batch testing can help you achieve the following business optimization goals:

1. Improve the success rate of adding friends

Only select real, communicable, non-banned usernames to perform the add action to reduce the number of failures.

2. Improve push hit rate

Messages are only sent to highly active and communicable users to avoid content loss or system blocking.

3. Reduce account risk control risks

Avoid high-complaint and highly sensitive accounts to protect the stable operation of your own operating accounts.

4. Save resource costs

Filter out invalid users, reduce the number of script runs, and reduce platform message usage.

5. Optimize delivery strategy

Stratify users based on tags, settingsCategory A/B groups carry out operational outreach with different frequencies and content.

 

6. Practical suggestions: How to continuously optimize the screening mechanism?

1. Regularly update test results

User status changes dynamically quickly. It is recommended that everyActivity and communication status are updated every 7 days or 15 days.

2. Establish a tag crowd pool

Use usernames with high activity and stable communication as "Frequently used user pool" to continuously operate and mark behavioral changes.

3. Introduce automated processes

After detection, the results are directly imported into automated tools (such as robot scripts, message scheduling systems, etc.), achieving no manual intervention in the entire process.

4. Reverse optimization based on behavioral data

Reversely analyze which type of users is more effective through message response rate, addition success rate and other data to further refine the filtering rules.

 

7. FAQs

Q1: Will it interfere with target users?

No, the Digital Planet detection mechanism will not send any messages or operations to the target account, and users will not be disturbed.

Q2: Can attributes such as language and country be recognized?

The platform supports through accountThe ID determines the country or common language behavior of some users and is used for subsequent regional delivery strategies.

Q3: Does it support custom label output?

Supported, tag combinations can be set according to business logic to export results to facilitate classification management.

Q4: Can it be combined with the group control system?

Yes, the test result format is compatible with mainstream group control systems, automated delivery platforms,SCRM system and other tools.

 

8. Conclusion: Screening is not only to filter out invalid users, but also to lay the foundation for subsequent delivery.

Telegram’s ecosystem is becoming more and more complex, especially in automation and mass messaging scenarios, and the platform’s behavioral rules are becoming increasingly strict. Whether you can identify which usernames are available and which ones are risky at the first time will determine whether your delivery action is effective and safe.

digital planet As a communication platform detection and tag identification service provider, we are providing services for a large number ofProvided by Telegram operation team, cross-border marketing company, and robot developersBatch testing and screening services, making the data cleaner, the behavior more stable, and the results more certain.

If you also need large quantitiesTelegram username detection, or are preparing for user cleaning, group messaging, data import, etc., please contact Digital Planet customer service, we will provide you with efficient, compliant, and low-cost detection solutions. Screen usernames once and save half the cost of trial and error.

 

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