How KALO IQ fake follower detection works

The KALO IQ verification team uses a combination of automated signals and human review to detect compromised creator profiles. Automated signals include: sudden follower spikes over short periods, engagement rate that does not match follower count at the tier level, comment patterns that repeat across posts (a signature of comment pods) and follower account quality metrics.

Human review looks at: comment authenticity (real responses to content versus generic compliments), follower account profiles (new accounts with no posts are a signal) and engagement distribution across posts (creators with purchased engagement typically show inconsistent engagement rather than steady performance).

Each creator receives a fake follower score on a scale of 0-100, where 0 means no detected suspicious activity and 100 means highly compromised. The score is visible on each creator's profile page.

Creators with scores above 40 are not surfaced in standard search results. They remain in the database for reference but the default search filters them out. Creators with scores above 70 are flagged for manual re-review by the verification team.

In the search filters, the Audience quality filter lets you set a maximum fake follower score. The default setting (which applies to all searches automatically) filters out creators above the 40 threshold.

You can tighten this by setting the filter to 20 or below for campaigns where audience quality is the primary concern - for example, high-value product launches where you need to be confident in the creator's real reach.

For any creator in the KALO IQ database, click their profile and scroll to Audience quality. This section shows: estimated real follower percentage, engagement quality score, comment authenticity rating and any flags raised during verification.

You can also use the free fake follower checker at kaloiq.com to run an independent check on any Instagram handle before committing to outreach.