Thousands of people communicate in niche communities every day. Among them, you can find active members, authors of relevant messages, and people who leave comments under posts.
The Telegram User Parser by GramGPT automates this process. One module provides three modes: collecting members, analyzing messages, and finding comment authors. Additional settings allow you to define which profiles should be included in the results.
What is a Telegram User Parser?
It is a tool for automatically collecting available information about members of selected communities.
GramGPT provides three ways to work:
- collecting a list of members;
- finding message authors;
- analyzing comments under posts.
You select the appropriate scenario, specify the sources, and configure the required parameters. Once processing is complete, the system generates a ready-to-use dataset.
How the parser works
First, select the connected accounts and one of the three available modes.
Next, add the required sources by username, link, or ID. They can be entered manually, added as a list, or selected from previously saved data.
Other parameters depend on the task. For example, you can limit the amount of data processed, specify an activity period, or enter relevant words and phrases.
Once started, GramGPT automatically performs the configured operation.
Collecting members
The first mode is designed for cases where you need an available list of members from a particular community.
You can specify one or more groups in the settings and define how much data should be processed for each source.
If you have many sources, you do not need to add them one by one — a list can be uploaded instead.
Additional parameters allow you to exclude unsuitable profiles and receive cleaner results.
Profile filtering
Before starting, you can define selection criteria.
For example, GramGPT allows you to:
- skip bots;
- exclude deleted profiles;
- exclude blocked and scam accounts;
- keep active members;
- select accounts with usernames;
- select profiles with photos;
- filter by Premium status.
This reduces the number of records that would otherwise need to be removed manually after processing.
Telegram User Parser by Messages
The second mode focuses not on the entire community but on people who actually participate in discussions.
The system analyzes available conversations and identifies message authors.
You can specify the analysis depth and time period. For example, you can process only messages from the last 30 days.
This approach is useful for large communities with many members where only a small percentage regularly participate in conversations.
Search by keywords
When analyzing conversations, you can specify relevant keywords or phrases.
For example, a single chat may contain discussions about marketing, cryptocurrency, development, design, and sales. If you are interested in only one topic, there is no need to process every discussion.
Simply enter the relevant terms. The system will find matching records and identify their authors.
If the field is left empty, processing can be performed without an additional topic filter.
Finding active members
Another scenario is finding people who have been active during a specific period.
This allows you to look beyond the total number of members.
For example, a community may contain 50,000 accounts, while only a few thousand have posted anything during the last month.
A time-based filter helps focus specifically on this active segment.
Telegram User Parser from Comments
The third mode is designed to work with discussions under channel posts.
GramGPT processes available comments and identifies their authors.
You can specify the number of posts to check and how deeply the discussions under each post should be processed.
This approach helps identify people who interact with content rather than simply reading a channel.
Comment analysis settings
Processing limits can be configured for each source.
For example, you can check the latest 50 posts and process up to 100 replies under each one.
A minimum text length can also be specified. This makes it possible to ignore very short replies when they are not relevant to the task.
If required, the content of a comment can be saved together with information about its author.
This is useful when you need to understand the context in which a person participated in a discussion.
Search within comments
Keywords and phrases can also be used when analyzing comments.
Suppose a post has 500 replies, but only some of them relate to the topic you are interested in.
Instead of processing every author, you can specify relevant terms and create a narrower selection.
This approach is particularly useful for large channels with active discussions.
Additional settings
Each mode has its own set of parameters.
You can change the processing volume, choose a time period, apply filters, and adjust the speed of operations.
Settings are applied before the task starts, so the results are generated according to the specified criteria from the beginning.
This reduces the need to manually sort large amounts of information afterward.
Three modes in one module
The appropriate method depends on your task.
Members — when you need a broad selection from a particular community.
Messages — when you are interested in people who actually participate in discussions.
Comments — when you need to find active authors under channel posts.
Each mode can be used independently.
For example, when researching a particular niche, you can first collect members from several relevant communities and then separately analyze the most active segment.
Building a targeted database
Built-in parameters help make the results more precise.
Instead of collecting the maximum possible number of records, you can define in advance which profiles should be included.
For example, you can keep only active profiles with usernames or find authors who recently discussed a particular topic.
This creates a narrower dataset that better matches the task.
Exporting results
Once processing is complete, the information can be saved for further use.
Common formats are supported, including CSV, JSON, and Excel.
The exported file can be sorted, analyzed, and used in other workflows.
If filters were configured in advance, less additional cleanup of the final table will be required.
How to choose the right mode
If you need a list of members from a specific community, the first option is the most suitable.
To find people who actually communicate, message analysis is a better choice.
If you are working with channels, you can identify authors through their comments.
Keyword search can further narrow the results and focus on a particular topic.
This means one tool can be used both for broad collection and more precise segmentation.
What is a Telegram Parser used for?
The module can be used for researching niche communities, analyzing activity, and creating targeted datasets.
For example, it can help determine who participates in a particular group, identify authors of recent discussions, or analyze people commenting on posts in selected channels.
Automation is especially useful when dealing with large amounts of information that would be inconvenient to process manually.
When using the collected data, you should take into account the platform's rules and applicable data-processing requirements.
GramGPT features
Three different workflows and additional selection parameters are available in one interface.
You can configure sources, limits, profile filters, activity periods, and keyword searches.
Once a task is complete, the results can be saved in a convenient format.
This allows the same module to work with both the overall membership of communities and narrower segments based on actual activity.
Buy Telegram User Parser
With GramGPT, you can buy a Telegram User Parser to automate data collection from selected sources.
The module works with group and chat members, message authors, and channel commenters. Filters, keyword search, limits, and result exports are available.
The parser combines all three modes in a single interface, allowing you to choose the appropriate collection method for each specific task.
