# TikTok ad library scraping and trend analysis

Scrape and classify TikTok's public ad library to identify trending music, sounds, effects, hashtags, and creative hooks in viral ad campaigns

Grade: X — Not a typed-decision job: needs written output, plain rules or lookups would do it, or needs raw image or audio. Also marks ideas that must not be built.. Score: 4.3.

Vertical: Agencies, media & creators.

Function: Ads.

Status: Seen in the wild.

Volume: routine.

Value: meaningful.

Risk: moderate.

Evidence: described plan.

Flags: check-fit.

- [Source](https://docs.ninjacat.io/changelog/ad-library-tiktok-and-linkedin-added)
- [Vertical](/verticals/agency_media_creator/)
- [Function](/functions/ads/)
- [Brand safety keyword classification](/use-cases/brand-safety-keyword-classification/)
- [Detect brand-name competitor bidding requests](/use-cases/detect-brand-name-competitor-bidding-requests/)
- [430 competitor ads a second](/use-cases/430-competitor-ads-a-second/)
- [Ad network and placement targeting classification](/use-cases/ad-network-and-placement-targeting-classification/)
- [Sponsor read detection](/use-cases/sponsor-read-detection/)
- [Fatigue language flag in comments](/use-cases/fatigue-language-flag-in-comments/)