Twitter (X.com) User Followings Extractor (Rich Metadata) cookieless

Overview

This actor performs a deep extraction of Twitter (X.com) user following relationships with enriched metadata, delivering structured datasets optimized for audience analysis and network intelligence. The extraction pipeline operates without cookie-based authentication, ensuring reliable data retrieval with high integrity. Output includes comprehensive engagement metrics, geographic distribution, account health indicators, and categorical segmentation suitable for downstream analytical workflows.

Data Dictionary

Field Name	Data Type	Definition
extraction_id	String	Unique identifier for the extraction operation, prefixed with flw_
scraped_at	String (ISO 8601)	UTC timestamp indicating when the data extraction was completed
external_id	String	Platform-specific user identifier, prefixed with usr_
following_count	Integer	Total number of accounts the target user is following
is_verified	Boolean	Indicates whether the account holds verified status (blue checkmark)
language_code	String	ISO 639-1 language code with regional variant (e.g., en_US)
last_activity	String (ISO 8601)	UTC timestamp of the most recent account activity detected
account_type	String	Classification of account category: personal, business, or creator
followings.active	Integer	Count of followed accounts with recent activity (within 30 days)
followings.inactive	Integer	Count of followed accounts with no recent activity (>30 days)
followings.private	Integer	Count of followed accounts with protected/private status
followings.public	Integer	Count of followed accounts with public visibility
engagement_metrics.interaction_rate	Float	Ratio of interactions to total followings (0.0 to 1.0 scale)
engagement_metrics.response_rate	Float	Ratio of reciprocal engagement from followed accounts (0.0 to 1.0 scale)
engagement_metrics.mutual_follows	Integer	Count of bidirectional following relationships
geo_distribution.primary_location	String	ISO 3166-1 alpha-2 country code of primary geographic presence
geo_distribution.coordinates.latitude	Float	Decimal latitude coordinate of primary location
geo_distribution.coordinates.longitude	Float	Decimal longitude coordinate of primary location
account_health	Float	Composite score indicating account authenticity and quality (0.0 to 1.0 scale)
content_categories	Array[String]	Taxonomic classification of content themes associated with the account
verification_level	Integer	Tiered verification status (0=none, 1=email, 2=phone, 3=identity)
trust_score	Float	Algorithmic trust rating based on behavioral patterns (0.0 to 1.0 scale)
activity_score	Float	Normalized measure of account engagement frequency (0.0 to 1.0 scale)
data_quality	String	Assessment of extraction completeness: high, medium, or low
processing_status	String	Current state of data processing: complete, partial, or failed

Sample Dataset

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

{
  "extraction_id": "flw_89a7b23c1d45",
  "scraped_at": "2025-12-21T15:22:33Z",
  "external_id": "usr_7834592106",
  "following_count": 892,
  "is_verified": true,
  "language_code": "en_US",
  "last_activity": "2025-12-20T08:15:42Z",
  "account_type": "personal",
  "followings": {
    "active": 751,
    "inactive": 141,
    "private": 284,
    "public": 608
  },
  "engagement_metrics": {
    "interaction_rate": 0.082,
    "response_rate": 0.234,
    "mutual_follows": 312
  },
  "geo_distribution": {
    "primary_location": "US",
    "coordinates": {
      "latitude": 40.7128,
      "longitude": -74.0060
    }
  },
  "account_health": 0.95,
  "content_categories": ["tech", "business", "lifestyle"],
  "verification_level": 3,
  "trust_score": 0.876,
  "activity_score": 0.792,
  "data_quality": "high",
  "processing_status": "complete"
}

Configuration Parameters

To ensure optimal data depth, configure the following:

Parameter	JSON Field Name	Data Type	Required	Description	Example
Username	userId	String	Yes	Twitter/X.com username (handle) without @ symbol	elonmusk

Analytical Use Cases

Researchers and data scientists can leverage this dataset for:

• Network Topology Analysis: Map follower-following relationships to identify influence clusters, community structures, and information diffusion pathways within social graphs.
• Audience Segmentation: Classify following patterns by account type, verification status, and engagement metrics to build targeted audience profiles for marketing intelligence.
• Influencer Discovery: Identify high-trust, high-activity accounts within specific content categories for partnership evaluation and outreach prioritization.
• Longitudinal Behavioral Studies: Track changes in following patterns, engagement rates, and account health over time to detect trend shifts and audience evolution.
• Geographic Market Analysis: Utilize geo-distribution data to understand regional audience composition and optimize location-based content strategies.
• Bot Detection & Data Quality Assessment: Apply trust scores, account health metrics, and activity patterns to filter synthetic accounts and ensure dataset integrity.

Technical Limitations

Important Considerations:

• Rate Limiting: Extraction throughput is subject to platform-imposed rate limits. Large-scale extractions (>10,000 followings) may require batched execution with delays between requests.
• Data Freshness: last_activity and engagement metrics reflect point-in-time snapshots. Real-time accuracy degrades for rapidly changing accounts.
• Private Account Restrictions: Accounts with protected status yield limited metadata. The followings.private count is estimated and may not include granular profile details.
• Geographic Precision: Coordinate data is derived from profile declarations and may not reflect actual user location. Accuracy varies by user disclosure practices.
• Verification Status Volatility: is_verified and verification_level fields reflect status at extraction time and may change due to platform policy updates.
• Content Category Inference: content_categories are algorithmically assigned based on bio text and recent activity; manual validation recommended for critical applications.
• Data Retention: Extracted datasets should be refreshed every 30-90 days to maintain analytical relevance, particularly for engagement and activity metrics.

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