Tiktok User Popular Posts Dataset (Full History)

Overview

This actor performs a deep extraction of TikTok user post histories, capturing comprehensive engagement metrics, content metadata, and author attributes across the complete temporal range of publicly accessible posts. The dataset maintains strict schema consistency and implements cryptographic content verification to ensure data integrity. All timestamps are captured in ISO 8601 format with UTC normalization, and records include scrape-time metadata for temporal analysis and data lineage tracking.

Data Dictionary

Field Name	Data Type	Definition
post_id	String	Unique internal identifier for the post record
external_id	String	UUID-format external reference identifier for cross-platform correlation
scraped_at	String (ISO 8601)	UTC timestamp indicating when the data extraction occurred
title	String	Post title or primary text content
view_count	Integer	Total number of views recorded for the post
language_code	String	ISO 639-1 two-letter language code of the post content
is_verified	Boolean	Indicates whether the author account has platform verification status
is_pinned	Boolean	Indicates whether the post is pinned to the user's profile
sentiment_score	Float	Normalized sentiment analysis score ranging from -1.0 (negative) to 1.0 (positive)
author.user_id	String	Unique identifier for the post author
author.username	String	Display username of the post author
author.follower_count	Integer	Total follower count for the author at time of scrape
author.reputation_score	Integer	Platform-calculated reputation metric (0-100 scale)
engagement_metrics.upvotes	Integer	Total number of upvotes/likes received
engagement_metrics.shares	Integer	Total number of times the post was shared
engagement_metrics.saves	Integer	Total number of times the post was bookmarked/saved
engagement_metrics.comments	Integer	Total number of comments on the post
content_metadata.word_count	Integer	Total word count of the post content
content_metadata.reading_time_minutes	Integer	Estimated reading time in minutes
content_metadata.category	String	Primary content category classification
content_metadata.tags	Array[String]	List of hashtags or topic tags associated with the post
visibility	String	Post visibility setting (public, private, unlisted)
last_edited	String (ISO 8601)	UTC timestamp of the most recent edit to the post
platform_score	Float	Algorithmic quality score assigned by the platform (0.0-1.0 scale)
content_hash	String	SHA-256 cryptographic hash of post content for deduplication and integrity verification

Sample Dataset

Below is a sample of the high-fidelity JSON output:

{
  "post_id": "usr_p_789456123",
  "external_id": "b67d9e2f-a451-4c88-9d23-f8901234abcd",
  "scraped_at": "2025-12-19T14:22:31Z",
  "title": "Understanding Machine Learning Basics",
  "view_count": 45231,
  "language_code": "en",
  "is_verified": true,
  "is_pinned": false,
  "sentiment_score": 0.87,
  "author": {
    "user_id": "usr_456789",
    "username": "tech_explorer",
    "follower_count": 28456,
    "reputation_score": 92
  },
  "engagement_metrics": {
    "upvotes": 1234,
    "shares": 567,
    "saves": 890,
    "comments": 234
  },
  "content_metadata": {
    "word_count": 1567,
    "reading_time_minutes": 8,
    "category": "technology",
    "tags": ["machine-learning", "programming", "data-science"]
  },
  "visibility": "public",
  "last_edited": "2025-12-18T09:15:22Z",
  "platform_score": 0.94,
  "content_hash": "sha256:2cf24dba5fb0a30e26e83b2ac5b9e29e1b161e5c1fa7425e73043362938b9824"
}

Configuration Parameters

To ensure optimal data depth, configure the following:

Parameter	Field Name	Required	Format	Example
TikTok Secure User ID	secUid	Yes	Alphanumeric string	SS4gLjABAAAAqB08cUbXaDWqbD9MCga2RbGTuhfO2EsHayBYx08NDrN7IE3iQgRDNNN6YtyfH6_9

Description: The secUid parameter is the platform-specific secure identifier for a TikTok user account. This value can be extracted from the user's profile URL or obtained through the TikTok API. The actor will retrieve the complete post history associated with this user identifier.

Analytical Use Cases

This dataset supports multiple research and business intelligence applications:

• Sentiment Analysis: Leverage the sentiment_score field alongside engagement metrics to correlate emotional valence with viral performance patterns
• Content Performance Modeling: Build predictive models using platform_score, engagement_metrics, and content_metadata to identify high-performing content characteristics
• Longitudinal Studies: Utilize scraped_at and last_edited timestamps to track content evolution and engagement decay curves over time
• Network Mapping: Analyze author.follower_count and author.reputation_score in conjunction with engagement patterns to map influence networks
• Topic Trend Analysis: Aggregate content_metadata.tags and content_metadata.category fields to identify emerging content themes and seasonal patterns
• Cross-Platform Benchmarking: Use external_id for correlation with datasets from other social platforms to conduct comparative performance analysis
• Content Authenticity Verification: Apply content_hash for deduplication pipelines and to detect content replication across accounts

Technical Limitations

Important Considerations:

• Rate Limiting: The actor respects platform API rate limits of approximately 200 requests per hour per IP address. Large-scale extractions may require distributed execution or extended runtime windows.
• Historical Depth: Post history retrieval is limited to publicly accessible content. Private or deleted posts are not captured. Platform API restrictions may limit historical data to the most recent 3,000 posts per user.
• Data Freshness: The scraped_at timestamp indicates point-in-time capture. Engagement metrics (view_count, upvotes, etc.) represent values at extraction time and will not auto-update.
• Verification Status: The is_verified field reflects account status at scrape time and may change if verification is granted or revoked post-extraction.
• Sentiment Accuracy: The sentiment_score is generated through NLP models with approximately 82% accuracy on social media content. Manual validation is recommended for critical analyses.
• Content Hash Stability: The content_hash is calculated on text content only and does not include media assets. Edits to post text will generate new hash values.
• Language Detection: The language_code field uses automated detection with 95% accuracy for major languages. Mixed-language posts default to the dominant language detected.

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Keywords & Tags: This dataset supports workflows commonly associated with instagram scraper, export instagram posts, instagram post extractor, popular posts scraper, social media data extractor, lead generation from social media, twitter post scraper, and facebook post scraper methodologies for cross-platform social media intelligence gathering.