Article summary
Design stratified samples, field coverage tests and missingness acceptance for Telegram demographic checks without treating inferred attributes as individual truth.
Acceptance for a Telegram gender-and-age task is not the count of men and women in an Excel file. It is whether the sample represents the intended data, coverage is transparent and unknowns remain honest. Age and gender results may depend on limited public signals or inference. They can support an approved aggregate study; they are not proof of an individual’s identity.
Write the research question before requesting profile fields
“Get more columns” is not a research question. A useful question might ask whether observable profile coverage differs substantially by acquisition source and whether that justifies a small test of two neutral creative approaches. If the result cannot change content, language or budget, there is no reason to run the profile task.
Stratify the acceptance sample
Sample real records by country, source, intake month, phone quality and customer stage. Preserve the input base for each stratum so high-quality established customers do not dominate. When a stratum is small, report insufficient confidence rather than merge it into a larger group and publish a falsely precise percentage.
| Stratum | Why observe separately | Required accounting |
|---|---|---|
| Country/code | Format and coverage may differ | Valid input and unknown count |
| Acquisition source | Public-signal richness differs | Original source proportion |
| New vs old cohort | Time affects account state | Intake and check date |
| Format exception | Separates input failure from field absence | Dedicated exception stratum |
Accept the identity columns first
An AIPUSH TG Gender and Age export can include phone, TG UserID, Telegram username, user offline time, active days, First Name, Last Name, TG VIP status, frozen status, avatar URL, age and gender. First confirm that phones reconcile, UserID and username are not collapsed, time values parse and Boolean states use consistent encoding.
Profile fields need three or four states
Gender and age cannot be reduced to male/female and a number. Preserve result, unknown, not applicable and processing error. Use broad approved age bands where appropriate and retain original nulls. Forcing unknowns into the largest category increases apparent coverage while systematically distorting smaller groups.
Use a different denominator for each metric
| Metric | Correct denominator | It cannot establish |
|---|---|---|
| Reconciliation | Valid unique inputs | Profile accuracy |
| UserID coverage | Joinable outputs | Account ownership |
| Gender coverage | Applicable, processable records | Personal identity truth |
| Age coverage | Applicable, processable records | Exact biological age |
| Avatar coverage | Observable accounts | Photo belongs to phone holder |
Test whether missingness is concentrated
Seventy percent overall coverage can hide a country with only twenty percent. Build country-by-source and source-by-cohort missingness matrices. If unknowns concentrate in one cell, the report must disclose the bias and must not generalize from the observed subset to the whole list. Acceptance thresholds should be stratified as well as global.
Use human review to assess explainability
Review does not need to prove every inferred attribute. It checks traceability, consistent null handling, compliance with the dictionary and unexplained changes across repeat runs. Reviewers should not receive unnecessary customer identity data; they record a decision reason rather than copy public avatars into another system.
Segmentation serves aggregate experiments
Results may help compare two neutral creatives across observable groups at an aggregate level. They should not determine price, credit, employment, account enforcement or whether an individual deserves service. Do not activate tiny cells, and do not create sensitive combinations likely to produce discriminatory treatment.
Give every field a lifetime and audience
Usernames, offline time, avatars and inferred profiles change. Store checked_at, source_task, confidence_or_unknown and delete_after, then stop using an expired observation for segmentation. Limit access to roles conducting the approved analysis; raw URLs and granular attributes should disappear earlier than aggregate reporting.
A signable acceptance finding
The final finding lists input, exceptions, reconciliation, field coverage, missingness patterns, repeat-test differences and allowed uses for every stratum. It also says what cannot be inferred. Passing acceptance does not declare the data “true”; it confirms that within a named time, sample and decision, the evidence is transparent and controlled enough to use.
