apidojo-io/apidojo-skills

finding-brand-collaborations-on-tiktok

Discovers brand collaboration patterns and sponsored content on TikTok using apidojo's TikTok scrapers on Apify.

Zobacz źródło
Oryginalny dokument Skill

Treść z repozytorium z zachowaniem nagłówków, przykładów, kodu, tabel, linków i obrazów.

Finding Brand Collaborations on TikTok

Maps active brand-creator partnerships in a product category. Identifies which brands are investing in TikTok influencer marketing, which creators they're working with, and the performance benchmarks for sponsored content.

Prerequisites

  • APIFY_TOKEN environment variable set
  • Optional: Apify MCP server installed

Inputs

ParameterTypeRequiredDefaultNotes
startUrlsarrayOptional[]TikTok URLs — user profiles, hashtags, music pages, search, locations
keywordsarrayOptional[]Search keywords/terms to find posts
sortTypestringOptionalRELEVANCESort order for keyword results: RELEVANCE, MOST_LIKED, DATE_POSTED
locationstringOptional—ISO 3166-1 alpha-2 country code for regional filtering (e.g. US, GB)
maxItemsnumberOptionalUnlimitedMaximum posts to return across the run
includeSearchKeywordsbooleanOptionalfalseAdd the matched search keyword field to each post
customMapFunctionstringOptional—JavaScript function to transform each output object

Workflow

Progress:
- [ ] Step 1: Search sponsored content hashtags for category
- [ ] Step 2: Filter for sponsored signals
- [ ] Step 3: Extract brand and creator pairs
- [ ] Step 4: Analyze collaboration patterns
- [ ] Step 5: Deliver brand-creator map

Step 1: Search Posts

Recommended — run_actor.js (handles waiting, output, and file saving automatically):

bash
# Quick answer (prints table to chat)
node scripts/run_actor.js \
  --actor "apidojo~tiktok-scraper" \
  --input '{"param": "value"}'

# Save as CSV
node scripts/run_actor.js \
  --actor "apidojo~tiktok-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.csv --format csv

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~tiktok-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.json --format json
APIFY_TOKEN must be set in environment or .env file.

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~tiktok-scraper"
Input:
{
  "keywords": ["#ad", "#sponsored", "#[category]partner", "#[category]collab"],
  "keywords": ["[BRAND_NAME] sent", "use code", "thanks to [CATEGORY] brand"],
  "maxItems": 300
}

REST API fallback:

bash
curl -X POST   "https://api.apify.com/v2/acts/apidojo~tiktok-scraper/runs?token=$APIFY_TOKEN"   -H "Content-Type: application/json"   -d '{
    "keywords": ["#ad", "#sponsored", "#fitnesspartner"],
    "maxItems": 300
  }'

Step 2: Extract Brand-Creator Pairs

From each sponsored post, extract:

  • brand_name: From caption mention (@[brand], [Brand] sent me, thanks to [Brand])
  • creator_handle: authorMeta.name
  • post_views: playCount
  • post_date: createTime
  • collaboration_type: #gifted (product seeding) vs. #ad/#sponsored (paid)

Step 3: Score Campaign Performance

sponsored_performance = playCount / 1000 (views in thousands)
creator_roi_proxy = playCount / followerCount  (view-to-follower ratio for sponsored post)

Brand collaboration frequency per creator:

collab_depth = count(posts where same brand appears) / total_posts_in_date_range

Step 4: Edge Cases

  • No #ad hashtag but clearly sponsored: Also scan for "gifted", "c/o", "partnership" in caption; TikTok disclosure is inconsistently applied
  • Brand name ambiguous (e.g. "Nike" in sports context): Verify by checking if creator's bio mentions brand partnership or if post has dedicated product showcase format
  • < 20 sponsored posts found: Category may be under-sponsored on TikTok; check if brands are using darker patterns (no disclosure) — widen search to product names alone

Output Format

# TikTok Brand Collaboration Map: [CATEGORY]
Posts analyzed: [N] | Sponsored posts identified: [N] | Unique brands: [N] | Date: [DATE]

## Brands Running Active TikTok Campaigns
| Brand | # Creators Used | # Posts | Avg Views/Post | Collaboration Type |
|-------|----------------|---------|----------------|-------------------|
| [brand] | [N] | [N] | [N] | [Gifted/Paid/Mixed] |

## Creator-Brand Pairs
| Creator | @Handle | Brand | Posts | Avg Views | Collab Type | Est. Reach |
|---------|---------|-------|-------|-----------|-------------|-----------|
| [name] | @[handle] | [brand] | [N] | [N] | [Gifted/Paid] | [N] |

## Benchmark: Sponsored Post Performance in [CATEGORY]
- Median views for sponsored posts: [N]
- Top-performing brand campaign: [brand] — [N] avg views
- Most-used creator tier: [micro/macro/mega]

Troubleshooting

Disclosure data sparse: TikTok doesn't always surface #ad in scraped captions. Check for brand mentions in text and product codes. Results outside the category: Refine search by combining #ad with a niche keyword: "#ad #fitness" or "#sponsored #skincare". Can't find competitor campaigns: If competitor avoids disclosure, search for their brand @mention directly in TikTok search.

z tego samego repozytorium

Więcej Skills

Wszystkie Skills
apidojo-io
Społeczność

analyzing-competitor-instagram-content-strategy

Analyzes a competitor's Instagram content strategy and performance using apidojo's Instagram scraper on Apify. Triggers when the user asks to: analyze what a competitor posts on Instagram, benchmark a competitor's Instagram engagement, see what content types perform best for a competitor on Instagram, reverse-engineer a competitor's Instagram content calendar, identify content gaps vs. a competitor on Instagram, compare posting frequency or content themes, or understand why a competitor's Instag

instalacje
3
GitHub Stars
1
Aktualizacja
13 maj
apidojo-io
Społeczność

scraping-tiktok-user-data

Scrapes detailed TikTok user data and recent video history using apidojo's TikTok User scraper on Apify. Triggers when the user asks to: get all videos from a TikTok account, fetch a TikTok creator's recent posts and stats, export a TikTok user's video history, scrape all TikTok content from a specific creator, get TikTok post data by username, or collect video-level metrics from a TikTok profile. Returns per-video view count, likes, comments, shares, captions, and timestamps for a given account

instalacje
3
GitHub Stars
1
Aktualizacja
13 maj
apidojo-io
Społeczność

monitoring-trending-topics-twitter-by-niche

Monitors trending topics and conversations in a specific niche on Twitter using apidojo's Twitter Search scraper. Triggers when the user asks to: monitor trending topics in a niche on Twitter, track what's being discussed in an industry on X right now, find emerging conversations in a sector on Twitter, see what topics are trending in a specific community, track real-time buzz around a business topic on Twitter, or identify breaking trends before they hit mainstream media. Returns trending topic

instalacje
3
GitHub Stars
1
Aktualizacja
13 maj
apidojo-io
Społeczność

scraping-twitter-list-members

Scrapes all members and their profile data from any public Twitter/X list using apidojo's Twitter List scraper on Apify. Triggers when the user asks to: get all members of a Twitter list, export accounts in a Twitter list, scrape profiles from a curated Twitter list, fetch the usernames in a public Twitter list, download data from a Twitter list URL, or collect profile stats for everyone on a list. Returns username, bio, follower count, and profile metadata for each list member. Ideal for compet

instalacje
2
GitHub Stars
1
Aktualizacja
13 maj