Article summary
Validate Telegram username and avatar results with field-level coverage, traceable join keys, exception queues, and evidence-based sampling.
A sound acceptance review does not demand a username, avatar, age, and gender value on every row. It proves that each result can be reconciled to an input number, the schema belongs to the selected task, missing values are treated consistently, and exceptions have a defined next step.
Checkpoint one: freeze the input baseline
Prepare an authorized TXT file with one international-format number per line. Record original rows, blank lines, format failures, and unique normalized numbers. Keep a source_row_id map before deduplication. Export-row counts become meaningful only when compared with this baseline.
| Baseline measure | Purpose |
|---|---|
| Original rows | Documents the received list size |
| Normalized rows | Shows which entries were submit-ready |
| Unique numbers | Provides the task-coverage denominator |
| Source mappings | Allows results to return to authorized records |
Confirm current task fields on the Telegram product page and browse related material in the username and avatar hub.
Checkpoint two: verify the schema, not the filename
The current username-and-avatar task can include phone number, TG UserID, TG username, offline time, active days, First Name, Last Name, TG VIP, frozen status, avatar URL, age, and gender. Compare the actual headers with that definition so a basic username export or another Telegram task is not accepted by mistake.
- Phone numbers should remain text and join to the TXT input.
- TG UserID and username must remain separate.
- Avatar URL, age, and gender should not collapse into one image-status column.
- Store task date and schema version with the delivery.
Checkpoint three: calculate coverage by layer
A single “completeness rate” hides the difference between account, profile, image-source, and analysis fields. Calculate TG UserID coverage, username coverage, avatar URL coverage, and avatar-analysis coverage separately. Use valid unique result rows as the denominator and exclude documented task exceptions.
| Metric | Calculation | What it cannot prove |
|---|---|---|
| Username coverage | Nonblank usernames / valid result rows | Identity or communication consent |
| Avatar URL coverage | Nonblank URLs / valid result rows | Image suitability for analysis |
| Analysis coverage | Rows with age or gender / rows with avatars | Verified personal attributes |
Checkpoint four: create an exception queue and sample it
Separate invalid input, missing whole rows, avatar URL failures, absent analysis values, and unexpected data types. Sample every category and trace it through the phone number to the TXT and authorized source record. Do not expand the dataset through unnecessary public searches. If a rerun is justified, build a separate TXT file and retain the original task ID.
Package three deliverables
- Untouched export: preserved read-only.
- Governed working table: adds source_row_id, task_id, checked_at, and review_status.
- Acceptance note: reports counts, coverage, exceptions, sampling, and unresolved issues.
The Telegram research hub contains broader task-selection boundaries. Give each recipient only the fields required for the documented purpose.
Frequently asked questions
Does low avatar coverage mean the task failed?
Not necessarily. Check whether account fields and task status are normal, then distinguish absent avatars from whole-row exceptions.
Can blank age and gender values be replaced with “unknown”?
A governed display column may use “unknown,” but the original result columns should remain blank and the transformation must be documented.
How should duplicate phone numbers be handled?
Deduplicate the normalized task input while keeping a mapping to every authorized source row. Do not discard legitimate context.
Must every row be checked manually?
Use automated rules first, then sample both normal results and every exception category. High-impact uses should not rely solely on automated outputs.
Acceptance means explainable evidence, not zero blanks
Coverage measures, exception queues, and source mappings show whether the dataset is fit for its intended use. Honest blanks and traceable decisions are more valuable than a table made artificially complete.
