Social Scraper Dataset Unifier

Normalize explicit datasets from existing social media scraper Actors into one deduped cross-platform output. This Actor does not scrape social platforms directly and does not run upstream scrapers by itself.

What It Does

It takes records or dataset outputs from existing Instagram, TikTok, X/Twitter, Facebook, YouTube, LinkedIn, Reddit, or similar social scraper Actors and emits one normalized schema for downstream analytics, automation workflows, dashboards, and exports.

Who It Is For

Marketing analysts, social listening teams, agencies, growth operators, and automation pipelines that already use strong platform-specific scrapers and need one predictable output format instead of separate schemas per platform.

Input

Provide sources, each with platform, optional actorId, optional datasetId, and either direct records or a dataset reference. You can also provide top-level records for a single mixed batch.

Output

Each row includes platform, source Actor attribution, source dataset ID, post URL or ID, author fields, normalized text, posted time, engagement counts, engagement score, engagement tier, field coverage score, record quality, missing core fields, hashtags, mentions, media URLs, canonical schema version, and a stable normalized key.

How It Works

The Actor maps common field names used by major social scraper Actors into one schema. It validates that each record has at least an identity or content signal, normalizes counts such as 1.2k, extracts hashtags and mentions, deduplicates by platform plus URL or post ID, computes an explicit engagement score, measures field coverage, classifies record quality, preserves source attribution, and emits only valid normalized rows.

The engagement score is deterministic: likes plus comments times two plus shares times three plus the base-10 log of views. It is a sorting and triage signal, not a claim about true reach or audience quality.

Record quality is deterministic too: a row is strong, usable, or thin based on whether core fields are present: identity, text, author handle, posted time, and at least one engagement signal. This is the main advantage over using separate scrapers directly: the output is not only normalized, it tells you whether the normalized row is complete enough for reporting or automation.

Example

{
  "sources": [
    {
      "platform": "instagram",
      "actorId": "apify/instagram-scraper",
      "datasetId": "abc123"
    },
    {
      "platform": "tiktok",
      "actorId": "clockworks/tiktok-scraper",
      "records": [
        { "url": "https://www.tiktok.com/@maker/video/1", "text": "Launch", "diggCount": "1.2k" }
      ]
    }
  ],
  "maxRecords": 10000
}

Pricing

Recommended PAY_PER_EVENT event unified_social_record_emitted: $0.02 per valid normalized social record. Upstream scraper costs are separate. Invalid records, duplicates, empty datasets, and charge-limited rows are not billable.

Limitations

This is not a replacement for Instagram, TikTok, X/Twitter, or Facebook scrapers. It does not run the upstream Actor by itself, does not autonomously discover social pages, and does not bypass login, CAPTCHA, platform limits, or upstream scraper terms. It is a normalization and workflow layer over datasets the user already has permission to process.

Integration

Use it after platform-specific scraping runs, in scheduled workflows, or as a cross-platform normalization stage before summarization, lead scoring, content intelligence, or reporting.