An Instagram follower export converts supported relationship rows into a consistent table for filtering, annotation, and snapshot comparison. The source matters: your own Meta account export and a limited public-profile research export have different permissions and coverage.
Use Meta’s official “Export your information” option for an account you own. Use a public-profile CSV only for fields currently available from a public source, with a recorded observation date and an honest description of coverage.
Recommended CSV columns
| Column | Purpose | Handling note |
|---|---|---|
| username | Primary comparison key for public rows. | Normalize case and remove URL prefixes. |
| display_name | Human-readable label at observation time. | May change and is not unique. |
| signal_type | Follower, following, addition, or missing-row category. | Use controlled values consistently. |
| observed_at | Timestamp for the snapshot or comparison. | Do not present it as an action timestamp. |
| source_note | Explains public source, limit, and collection method. | Keep methodology with the dataset. |
How to prepare a comparison-ready export
- Choose the correct source based on whether you own the account.
- Keep only fields needed for the stated research purpose.
- Normalize usernames, dates, and signal labels.
- Remove duplicates and document snapshot limits.
- Store files securely and delete them when the purpose ends.
- Compare datasets using the username column and review ambiguous changes manually.
Data minimization and privacy
A public field is not automatically risk-free. Avoid collecting unnecessary bios, contact details, location references, or sensitive inferences. Limit access, define a retention period, and respect removal requests. Do not sell or repurpose exports beyond the disclosed research purpose.
Frequently asked questions
Is the sample CSV a complete follower list?
No. Sample output demonstrates structure only. A live public-profile export could also be capped, cached, sampled, or incomplete depending on the compliant source.
Can I compare two CSV files?
Yes. Normalize the username column, then calculate later-only rows for possible additions and earlier-only rows for possible removals. Keep the two observation dates with the result.
Follow the complete export, cleaning, spreadsheet, and comparison workflow.
Read the CSV guide