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Deep Influencer Analyzer Actor

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Deep Influencer Analyzer Actor

Deep Influencer Analyzer Actor

Deep analysis of influencers for partnership evaluation, including audience quality assessment, engagement authenticity detection, content style analysis, brand safety scoring, growth trend analysis, etc. Audience Quality Engagement Content Style Brand Safety Growth Trends Partnership Eval

Pricing

from $0.01 / 1,000 results

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0.0

(0)

Developer

The Howlers

The Howlers

Maintained by Community

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2

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18

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0

Monthly active users

2 days ago

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Influencer Analyzer

Scores influencers for partnership fit from real profile metrics and recent posts: audience quality signals, engagement authenticity, content stats, brand safety (caption keyword scan) and an overall partnership potential. Optional AI content assessment with your own OpenAI or Anthropic key.


Important: where the data comes from

This actor does not scrape Instagram, TikTok, YouTube, LinkedIn or X itself. It analyzes data you give it:

  • Per influencer in the input: followers, following, postsCount and posts (recent posts with caption, likes, comments, and optionally url, timestamp, type), or
  • A discovery dataset (discoveryDatasetId) whose items already carry real metrics, for example from the influencer-discovery actor run with demo mode off.

If an influencer has no usable data, the actor writes an error row (status: "error", errorCode: "NO_PROFILE_DATA") explaining what to supply, and does not charge for it. It never fills in placeholder numbers. Items that came from a demo-mode discovery run are skipped the same way (errorCode: "SAMPLE_SOURCE_DATA").

Any score the supplied data cannot support is returned as null and listed in unavailableFields.


Quick start

Real analysis with supplied data

{
"influencers": [
{
"platform": "instagram",
"username": "some_creator",
"followers": 12000,
"following": 800,
"postsCount": 300,
"posts": [
{ "caption": "New drop #style link in bio", "likes": 400, "comments": 20 },
{ "caption": "Morning run", "likes": 350, "comments": 15 },
{ "caption": "Giveaway time #giveaway", "likes": 900, "comments": 120 }
]
}
],
"analysisModules": ["audienceQuality", "engagementAuthenticity", "contentStyle", "brandSafety"]
}

Preview the output format (demo mode)

Set "demoMode": true. Every row is labeled isSampleData: true with random numbers that do not describe the real account. Demo rows are not charged.


What each module needs

ModuleNeedsOutput
audienceQualityfollowers and followingHeuristic score from follower/following ratio and post count
engagementAuthenticity3 or more posts with likes/commentsLike-to-comment ratio, consistency, spike detection
contentStylepostsContent types, hashtag and caption stats (no style score)
brandSafetypost captionsKeyword scan with your flagKeywords added to the defaults
growthTrend, competitorOverlap, sponsorshipHistorynot implemented yetListed in unavailableFields

analysisDepth: "comprehensive" with aiProvider and a matching API key adds an AI-written content assessment based on the supplied posts.


Input parameters

ParameterTypeDefaultDescription
influencersarray-Influencers to analyze, with optional real metrics and posts
discoveryDatasetIdstring-Dataset ID from the influencer-discovery actor
analysisDepthstring"standard"quick, standard or comprehensive
postsToAnalyzeinteger12Maximum supplied posts analyzed per influencer
analysisModulesarray4 core modulesWhich modules to run
brandContextobject-Passed to the AI assessment
aiProviderstring"none"openai, anthropic or none (BYOK)
openaiApiKey / anthropicApiKeystring-Your key for the AI assessment
flagKeywordsarray-Extra brand safety keywords
demoModebooleanfalseReturn labeled sample data instead of analyzing
webhookUrlstring-Receives results and errors when the run finishes

Pricing

Pay per event:

EventWhen it is chargedPrice
Influencer AnalyzedAn influencer with at least 2 modules scored from real data$0.05

Not charged: demo mode rows, error rows, and partial rows where fewer than 2 modules could be scored (these are marked charged: false). Apify's standard actor start and per-result platform events still apply.


Output fields

status (ok, error or sample), isSampleData, dataSource, charged, profile metrics, overallScore (average of the scores that could be computed), per-module scores and details, partnershipPotential (high, medium, low, not_recommended or insufficient_data), strengths, concerns, recommendations, suggestedCollabTypes, unavailableFields.


Built by John Rippy | Actor Arsenal