/apify-ultimate-scraper

Influencer vetting workflows

Instagram creator vetting

When: User wants to evaluate an influencer's profile, audience, and engagement quality.

Pipeline

1. Get profile data -> apify/instagram-profile-scraper
   • Key input: usernames (list of handles)
2. Analyze engagement -> apify/instagram-comment-scraper
   • Pipe: results[].latestPosts[].url -> directUrls (pick 3-5 recent posts)
   • Key input: directUrls, resultsLimit

Output fields

Step 1: username, fullName, followersCount, followsCount, postsCount, biography, isVerified, latestPosts[] Step 2: text, ownerUsername, timestamp (scan for bot patterns: generic praise, emoji-only, irrelevant content)

Gotcha

High follower count with low comment quality suggests fake followers. Compare comment sentiment to post content.

Cross-platform influencer discovery

When: User wants to find an influencer's presence across multiple platforms.

Pipeline

1. Search across platforms -> tri_angle/social-media-finder
   • Key input: query (influencer name or handle), platforms

Output fields

Step 1: platform, profileUrl, username, followers, isVerified

TikTok creator vetting

When: User wants to vet TikTok creators by niche or handle for partnership fit based on engagement rate and content quality.

Pipeline

1. Get profile metrics -> clockworks/tiktok-profile-scraper
   • Key input: profiles (username array), resultsPerPage
2. Pull recent videos -> clockworks/tiktok-video-scraper
   • Pipe: results[].authorMeta.name -> profiles
   • Key input: profiles, maxItems

Output fields

Step 1: authorMeta.name, authorMeta.fans, authorMeta.heart, authorMeta.video Step 2: diggCount, playCount, commentCount, shareCount, createTimeISO, hashtags, text

Gotcha

TikTok engagement rate must be calculated manually: (diggCount + commentCount + shareCount) / playCount. The Actor does not return a pre-calculated ER field.

YouTube channel audit

When: User wants to audit YouTube channels for subscriber growth, average views, and topic consistency before sponsorship.

Pipeline

1. Get channel overview -> streamers/youtube-channel-scraper
   • Key input: startUrls (channel URLs), maxResults
2. Pull video metrics -> streamers/youtube-scraper
   • Pipe: results[].channelUrl -> startUrls
   • Key input: startUrls, maxResults
3. Analyze content themes -> curious_coder/youtube-transcript-scraper
   • Pipe: results[].url -> video URLs (pick 5-10 recent videos)
   • Key input: video URLs

Output fields

Step 1: channelName, numberOfSubscribers, channelTotalViews, channelUrl Step 2: videos[].viewCount, videos[].likeCount, videos[].title, videos[].publishedAt Step 3: transcript (raw text for AI topic classification)

Cross-platform hashtag discovery

When: User wants to discover new influencer candidates across Instagram, TikTok, and YouTube using niche hashtags for a unified shortlist.

Pipeline

1. Instagram hashtag scrape -> apify/instagram-hashtag-scraper
   • Key input: hashtags (array), resultsLimit
2. TikTok hashtag scrape -> clockworks/tiktok-hashtag-scraper
   • Key input: hashtags (same array), maxItems
3. YouTube hashtag scrape -> streamers/youtube-video-scraper-by-hashtag
   • Key input: hashtags (same array), resultsLimit

Output fields

Step 1: ownerUsername, followersCount, profileUrl, likesCount, commentsCount Step 2: authorMeta.name, authorMeta.fans, playCount, diggCount, shareCount Step 3: channelName, numberOfSubscribers, viewCount, channelUrl

Gotcha

Each platform returns platform-specific field names. Normalize to a common schema (username, platform, followersCount, avgEngagement, profileUrl) in a downstream merge step before scoring.