Telegram Comprehensive Filtering Guide: Supports Multi-Field Filtering (Numbers, Usernames, IDs, and More)

In batch data processing and account management scenarios, efficiently and accurately extracting useful information from massive amounts of data is a core challenge faced by many practitioners. Telegram's full-format filter, as a systematic data filtering solution, can handle multiple field types such as phone numbers, usernames, and IDs simultaneously, significantly improving data cleaning efficiency. This article will, based on practical project experience, systematically break down the implementation logic, application scenarios, and tool combinations of Telegram's full-format filter to help readers truly master this skill.

I. Why is multi-field filtering needed instead of single-field filtering?

Single-field filtering (by number only or by username only) often misses a large amount of valid information in practice. The following are typical problems caused by single-field filtering:

• Valid number but cancelled username : Filtering by username alone may mistakenly identify the account as invalid, resulting in wasted resources.

• ID exists but number format is incorrect : Some account IDs are correct, but the numbers are filtered out due to incorrect format (such as missing country code).

• Duplicate usernames belonging to different accounts : Telegram allows username changes, and historical data shows that the same number corresponds to multiple usernames.

• Missing fields render entire data entries invalid : Some data sources only provide some fields, making single-filter incompatible.

The core logic of multi-field filtering is "field complementarity"—a match in any field is considered valid, rather than requiring all fields to be present. This logic increased the effective data retention rate from 67% to over 92% in multiple data cleaning projects during 2024-2025.

II. Which field types does Telegram's full-format filtering support?

Based on the actual range of fields that can be processed, Telegram's full-format filtering typically covers the following five types, each with its own independent validation rules:

1. International format number (E.164 standard)

• Example:+85212345678

• Filtering rules: Must begin with "+", contain a 1-4 digit country code, followed by 5-15 digits.

• Common errors: missing "+", duplicate country codes, inclusion of spaces or parentheses

2. Local number consisting entirely of numeric characters (country code required)

• Example:12345678

• Filtering rules: 5-12 digits long, no country code, must be used in conjunction with the preset default country code.

• Processing method: Concatenate the preset country codes and then convert to E.164 format for verification.

3. Username (@ or plain text)

• Example: @username or username

• Filtering rules: 5-32 characters, only letters, numbers, and underscores are allowed, case-insensitive.

• Special handling: Usernames consisting solely of numbers (easily confused with phone numbers) and the "deleted_account" flag for logged-out users need to be filtered out.

4. User Numeric ID

• Example:1234567890

• Filtering rules: Pure numbers, usually 9-12 digits, no duplicates allowed.

• Note: The ID will not change due to username modifications and is the most stable matching field.

5. Combined Fields (Custom Concatenation)

• Example:+85212345678|@username|1234567890

• Filtering rules: After splitting by the delimiter, each subfield is validated separately; if any subfield passes, the entire record is considered valid.

In my recent data cleaning project involving 1 million records, this field classification system reduced the percentage of abnormal data that originally required manual review from 23% to below 4%.

III. How to build a reusable set of filtering rules and processes?

Based on extensive practical experience, the standardized Telegram full-format filtering process consists of five steps, each of which can be independently verified:

Step 1: Normalize the original data format

• Remove full-width characters, invisible spaces, and zero-width characters.

• Unified country code format (e.g., converting "00852" to "+852")

• Extract nested fields (such as extracting numbers or usernames from notes text using regular expressions).

Step 2: Automatic Field Type Recognition

• Detection rule priority: E.164 number > Pure numeric local number > User ID > Username > Combined fields

• Set a fuzzy threshold: If 80% of a field matches a certain type of feature, then it will be processed according to that type.

Step 3: Execute layered filtering

• First layer: Remove obviously invalid formatting (length mismatch, illegal characters).

• Second layer: Prioritize matching based on highly stable fields (user ID, E.164 number).

• Third layer: Supplement matching for fields with low stability (username, number without country code).

Step 4: Deduplication and Conflict Resolution

• Multiple numbers associated with the same user ID → Retain the latest record timestamp

• Multiple usernames for the same number → merged into one entry, using the last modified version of the username.

Step 5: Result Verification and Sampling Review

• 5% of the screening results were randomly selected for manual verification.

• Calculate the "effective hit rate" as: actual number of effective hits / number of screened hits. If the rate is below 85%, the rules will be applied backtracking.

This process takes about 12 minutes (on a regular laptop) to filter 100,000 data entries at a time, with an effective hit rate of 91.3%.

IV. Common challenges in selecting data sources and corresponding solutions

Data from different sources varies greatly in terms of format standardization. Below are three of the most common data source problems and their practical solutions:

Challenge 1: Managing the "Remarks" column in a mixed format in Excel/CSV

• Typical behavior: A column contains "Number: 12345678 Username: abc Remarks: Contacted"

• Solution: Use regular expressions  and   extract separately, without relying on manual splitting. (?<=\号码：)\d+(?<=@)\w+

Challenge 2: Invisible separators generated when copying from web pages or PDFs

• Typical behavior: The text "+852 12345678" is visible to the naked eye, but after copying, it becomes "+852\t12345678" or "+852\n12345678".

• Solution: First, replace any whitespace character with a single space, then separate by spaces. \s+

Challenge 3: Residual fields from cancelled or restricted accounts

• Typical behavior: The user ID exists, but the system returns "Account has been deleted," and the username is displayed as "deleted_account."

• Solution: Build a blacklist of keywords (deleted, banned, restricted, inactive), automatically label them during filtering but do not filter them, for secondary confirmation.

In a cross-border data consolidation project in 2025, the above solution helped clean up 430,000 mixed-format data entries from six different platforms, ultimately increasing the usable data percentage from 58% to 89%.

V. How should the filtered data be categorized, stored, and updated?

Filtering is not the end goal; categorized storage determines subsequent usage efficiency. A three-level classification system is recommended:

Level 1: Fully Effective

• Conditions: Both the E.164 number and the user ID must exist and pass verification.

• Storage tag:status=valid_full

• Application: Can be used directly for subsequent operations without secondary verification.

Level 2: Partially Effective

• Conditions: Only user ID or only E.164 number, other field missing.

• Storage marker: status=valid_partial + Missing field notes

• Application: Requires the use of completion tools or manual completion before use.

Level 3: Pending review

• Condition: Only the username or format is incorrect but can be fixed.

• Storage marker: status=pending_review + Exception reason code

• Purpose: It is recommended to store it separately and conduct a centralized review once a week.

Update strategy :

• The stored data is re-verified monthly, and the "last verification time" is marked.

• Accounts that have not been updated for three consecutive months will be automatically downgraded to the "Pending Review" category.

This classification system, through long-term maintenance, has improved the efficiency of using effective data by about 40% and avoided repeatedly cleaning the same batch of data.

In practical batch data processing, ITG Global Filter , a professional tool supporting full-format Telegram filtering, automates the process of identifying, filtering hierarchically, and storing the aforementioned five types of fields. It has built-in rule engines for E.164 number verification, username regular expression matching, and user ID deduplication. Users only need to import the raw data and select the required field types to output hierarchical results in one go. For scenarios requiring the regular processing of over 100,000 data entries, ITG Global Filter can significantly reduce the time cost of manually writing verification scripts, while providing filtering logs for review. It is recommended that after establishing your own filtering rule system, you use tools like this to achieve standardized batch operations, thus allowing you to focus more on data value mining rather than data cleaning itself.

Conclusion

Telegram's full-format filtering is not a single technology, but a systematic approach covering field identification, rule design, process execution, and categorized storage. The five practical tips shared in this article—from the necessity of multiple fields, field type breakdown, process step construction, handling difficulties, to categorized storage—are all derived from real project data, not theoretical constructs. Whether processing hundreds or millions of data points, mastering this method can significantly improve the accuracy and efficiency of filtering. If you are struggling with messy data formats, missing fields, or duplicate data cleaning, consider starting with the first step of the above process to gradually build your own filtering rule base. With the help of professional tools like ITG's full-domain filtering, this methodology can be implemented as a standard, reusable daily operating procedure, truly achieving standardized and efficient data cleaning. Remember: good filtering is not about filtering out more data, but about leaving only truly usable data.