Definition
TikTok follower quality signals are data indicators derived from API-accessible metrics that distinguish genuine audience growth from purchased or bot-inflated follower counts.
In Depth
Bought followers exhibit consistent patterns detectable via API data. Three signals are reliably API-accessible without accessing individual follower profiles: 1. Engagement rate anomaly: genuine creators with 100k-500k followers have 2-6% engagement rates. A creator with 200k followers and 0.3% engagement rate has a high probability of purchased followers. Calculate using the engagement_rate formula (likes + comments + shares) / views across the last 10 videos. 2. Follower velocity vs content cadence: API-accessible `followerCount` vs posting frequency. A creator who posted 3 videos in January and gained 80k followers, then posted 40 videos in February and gained 2k followers, has a suspicious January spike inconsistent with organic growth. 3. Video-to-follower ratio: organic TikTok growth requires consistent posting. A creator with 500k followers and only 8 total videos has an implausible follower-to-content ratio for organic growth. Genuine creators at 500k typically have 100-500+ videos. ```python def follower_quality_flags(profile: dict, videos: list) -> list: flags = [] followers = profile["stats"]["followerCount"] video_count = profile["stats"]["videoCount"] if video_count > 0 and followers / video_count > 20000: flags.append("high_follower_to_video_ratio") avg_er = sum(engagement_rate(v) for v in videos) / len(videos) if followers > 50000 and avg_er < 1.0: flags.append("low_engagement_rate") return flags ```
Example Usage
A brand screening 300 creator applicants used API-based follower quality checks to eliminate 47 creators with engagement rates below 0.8%, saving an estimated $28,000 in misallocated influencer spend.
Platforms
TikTok Follower Quality Signal is relevant across the following platforms, all accessible through Scavio's unified API:
- tiktok