Definition and standard explanation of tg active data in LLM: How to obtain high-quality active account sources in batches

In overseas operations, cross-border marketing and Telegram community promotion, tg active data is no longer an unfamiliar word. But for most teams, what is “active”? What is the difference between “registration” and “valid”? More importantly, when large language models (LLM) begin to be used for training and inference, how tg active data is defined and referenced becomes a new topic. This article will start from the perspective of LLM training data, dismantle the core dimensions of tg active data, and introduce a set of batch screening solutions that can be implemented to help operators and developers avoid data traps and efficiently obtain high-quality active number sources.

What is tg active data? Why is it important for both operations and LLM?

tg activity data, simply put, is label data generated by detecting whether the Telegram account** has generated substantial interactive behaviors** (such as being online, sending and receiving messages, and participating in group activities) within a specified time window. It is neither a simple “registration test” - which can only confirm that the number is bound to a TG account; nor is it a “validity test” - which pays more attention to whether the number can currently receive messages. The core of active data is: This user is really using it recently.

Registered vs Active vs Valid: The difference between three key metrics

Metrics	Meaning	Typical uses	Operational implications
Registration (activation)	The number has been registered with Telegram and has an account	Minimum threshold screening to avoid invalid numbers	It only means that there is an account, and it cannot be judged whether it is online
Active	There are traces of usage within a specified number of days (such as 7 days/30 days)	Private message promotion, community membership, precise reach	Reflect the real usage status, the conversion rate is significantly higher
Valid	The number can currently receive messages (such as verification codes can be sent)	Trigger operations (such as verification codes, notifications)	It does not represent recent use and may not have been logged in for a long time

Only by understanding the differences between these three can you reasonably choose the detection type in different business scenarios. For example, when sending group private messages, the open rate and reply rate of active accounts are usually 3-5 times higher than those of registered accounts; when doing sign-ins or maintaining accounts, valid accounts are also sufficient.

Why both operators and LLM developers need tg active data

• Operation staff: tg active data is the bullet for refined operations. If you place ads, attract groups, and reach out via private messages, calling a large number of “zombie accounts” will not only waste costs, but may also trigger platform risk control. With active tags, real users who have been online recently can be accurately identified to improve ROI.
• LLM Developer: tg active data can be used as feature labels for training corpus. For example, when building a dialogue model, training with active user dialogue data makes it easier for the model to learn real interaction patterns. In addition, tg active data can also be used as a verification basis for model output - for example, whether the user portrait generated by a model is consistent with the gender/age distribution in tg active data can be used as a dimension to evaluate bias.

How does LLM reference tg active data as a standard interpretation?

When large language models are used to interpret or generate descriptions about Telegram users, tg activity data is usually not directly exposed as raw fields, but is abstracted into tags, anchors, or constraints. The following are the two most common types of citations.

As the feature label of LLM training data

In the training corpus, each user-related text can be accompanied by a meta tag, for example:

[user_active: 30d][gender: male][age_group: 25-35] 用户A在群组中发言：“……”

LLM can learn: Text with the “active: 30d” tag usually comes from currently active users whose speaking style and topic preferences are significantly different from users who have not logged in for a long time. In this way, the model can also distinguish the language patterns of active and inactive users during the inference stage.

As the fact anchor and verification basis for LLM output

When using LLM to generate user profiles or behavior predictions, tg activity data can serve as a “fact anchor.” For example:

• User input: “What products would you recommend to male users around 30 years old?”
• LLM can reference the gender and age fields in tg active data and output: “Based on tg active data, among male users aged 25–35, the interaction rate on games and financial topics is the highest…”

The role of tg active data here is to constrain the model not to make random assumptions, so that the output is closer to the real data distribution. At the same time, LLM itself can also be required to include the data source when outputting, for example, indicate “the data comes from tg active detection results”.

What are the core detection dimensions of tg active data?

When obtaining tg active data in batches, the following dimensions usually need to be detected. Only by understanding the meaning of each dimension can you correctly configure the screening task.

Dimensions	Field description	Usage scenarios
Activation status (registration detection)	Whether the number is registered with Telegram	Basic filtering, eliminating invalid number segments
Active window	Is there any online/message behavior in the last N days	Private message promotion, community operation, placement targeting
Gender	Gender inferred by the model (male/female/unknown)	Population targeting (such as male users preferentially promote games)
Age	Age inferred by the model (such as 25–30, 30–35, etc. range)	Precision marketing, content strategy
tgid (Telegram ID)	The user’s unique internal ID	Connecting with other systems, deduplication, and secondary marking

Note: The gender and age fields are model inference results, not officially provided data. The accuracy has a certain range, and you need to fully understand its limitations when using them.

Active window: How to specify a range of days?

When submitting tg active detection tasks on screening platforms such as KK-DATA, you can usually customize the active window, for example:

• 7 days active: There has been online or messaging behavior in the last 7 days. Suitable for scenarios that require high immediacy (such as event notifications).
• 30 days active: active within the past month. It balances sample size and timeliness and is the first choice in most operational scenarios.
• 90 days active: usage records in the past three months. Suitable for market statistics or long-term user portraits.

The shorter the window, the higher the freshness of the data, but the pass rate may be lower; the longer the window, the larger the sample size, but some low-frequency users may be mixed in. It is recommended to flexibly adjust according to the target market and use cycle.

Gender and age fields: how to use them for crowd targeting?

Gender and age fields in tg active data can help operations teams achieve higher-precision targeting. For example:

• To promote women’s beauty products in the Southeast Asian market, you can filter numbers for “women + 18-35 years old + 30 days active”.
• To promote the game APP in the European and American markets, you can filter the numbers of “male + 25-40 years old + active for 7 days”.

These fields are available directly from the CSV export of the screen size results, with no additional modeling required. But please note: These inference results are not 100% accurate. It is recommended to make a comprehensive judgment based on other dimensions (such as group speech content).

How to obtain clean tg active data in batches?

To obtain high-quality TG active data in batches, you need to go through the complete pipeline of “Generation → Screen Number → Deduplication → Export”. The following takes the KK-DATA platform as an example to introduce the specific steps.

Global number generation: Build initial number source pool

The first step is to generate the number to be tested. KK-DATA provides the global number generation function, which supports the random generation of numbers in 240+ countries/regions. It can also generate or upload customized CSV by number segment.

• Random generation: Specify the country code and quantity, and generate a global number segment with one click.
• Number segment generation: Generate based on operator or number segment prefix, suitable for targeting countries.
• CSV Import: There is already a local number database, upload it directly.

Generation is free and does not consume your balance. It is recommended that the generation level be 3–5 times that of the final active number needed, since the pass rate of screen numbers is usually only 20%–40% (depending on the active window and country).

Screen number task configuration: select tg active detection type

When creating a screening task in the application console https://app.kkdata.cc/, select the “Telegram” platform and check the following detection types:

• tg activation (registration test)
• tg active (customizable days window, such as 7 days, 30 days)
• tg gender data (including gender, age, avatar and other fields)

Before submitting a task, the system will display an estimated cost. Billed by item, no package threshold, just pay for what you use. Please refer to the real-time price of the console for the unit price of the platform. The prices are different for different detection types.

Data deduplication warehouse: avoid duplicate detection and waste of balances

KK-DATA has a built-in data deduplication warehouse. When the same number is used across tasks, the system will automatically identify and skip the detected records to avoid repeated deductions. This is very practical for long-term and multi-wave screening of numbers - for example, if you screen 1 million for the first time, and want to screen another 500,000 for the second time, if the numbers overlap, the overlapping part will not be deducted again.

After the screening is completed, the results support exporting to CSV or TXT format, including all checked fields.

Common misunderstandings and precautions when obtaining tg active data

In actual use, many teams have encountered pitfalls. The most common misunderstandings are listed below to help you avoid detours.

Value comparison of tg active data in different scenarios

Scenario	Core Purpose	Recommended Active Window	Key Fields	Typical ROI Improvement
Community operation (attracting new members to the group)	Invite recently active users to join the group to increase activity	7 days or 30 days	Open + active	Increase the group membership rate by 2–3 times
Private message promotion (1-to-1 reach)	Send promotional messages to active users to reduce blocking rate	30 days	Active + gender + age	Increase response rate by 4–6 times
LLM training corpus annotation	Attach active labels to dialogue data to improve model implementation	No strict limit, 30 days recommended	Active status + gender + age	Improve the model’s fit to real user behavior by 30%
Market research and user portraits	Understand the active proportion, gender and age distribution of TG users in a certain region	90 days	Active + gender + age	More accurate strategy formulation

FAQ

**Q: What is tg active data? ** Answer: tg activity data refers to label data formed by detecting whether a Telegram number is online, sending and receiving messages, or participating in group interactions within a specified time window. Different from only detecting “whether to register”, activity data can better reflect the user’s real usage status.

**Q: What is the difference between TG active data and ordinary registration number? ** Answer: The registration number only means that the number has a TG account, while the activity data means that the account has been used substantively in the recent past. For overseas operations and LLM training, active data is more practical and valuable.

**Q: Can tg active data be directly used for LLM training? ** Answer: Yes. Fields such as active status, gender, and age in tg active data can be used as feature labels for training corpus, and can also be used as a basis for verification of model output results. However, it should be noted that the data is the result of model inference, the accuracy is limited, and should not be regarded as the absolute true value.

**Q: Where can I obtain TG active data in batches? ** Answer: Batch testing can be carried out through screening platforms such as KK-DATA. The platform supports specifying active windows (such as 7 days, 30 days), and the data can be exported in CSV or TXT format after the detection is completed. For specific billing details, please see the real-time price on the console.

**Q: How long can a tg active data detection be used? ** Answer: The active status changes over time, and it is recommended to update it regularly according to the frequency of use. For operational delivery, it is generally recommended to re-inspect every 1-2 months. For LLM training, it needs to be arranged flexibly according to the training cycle and data freshness requirements.

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If you need to obtain high-quality TG active data in batches, you can directly log in to the console to start using it: 👉 [Log in to the console to start filtering] (https://app.kkdata.cc/) If you have any operational questions or need customized solutions, please feel free to contact customer service in both directions: https://t.me/kkdata_robot For more product details, please refer to the official website https://kkdata.cc/ and documentation https://docs.kkdata.cc/.