Twitter (X.com) List Members Extractor cookieless (Rich Metadata)

Overview

This actor performs a deep extraction of Twitter list membership data, capturing comprehensive user profiles, engagement metrics, and geolocation attributes without requiring authentication cookies. The extraction pipeline prioritizes data integrity through structured validation layers and delivers high-fidelity JSON outputs suitable for downstream analytics, CRM integration, and machine learning workflows. Reliability is maintained through rate-limit detection, retry logic, and versioned extraction protocols.

Data Dictionary

Field Name	Data Type	Definition
list_id	String	Unique identifier for the Twitter list being scraped
member_count	Integer	Total number of members present in the list at extraction time
scraped_at	String (ISO 8601)	UTC timestamp indicating when the extraction was initiated
external_id	String	Internal tracking identifier for the extraction job
members	Array[Object]	Collection of user profile objects representing list members
members[].user_id	String	Unique Twitter user identifier
members[].screen_name	String	Twitter handle (username without @ symbol)
members[].display_name	String	User's public display name as shown on profile
members[].is_verified	Boolean	Indicates whether the account has Twitter verification status
members[].language_code	String	ISO 639-1 language code for user's primary language setting
members[].followers_count	Integer	Total number of followers for the user account
members[].location.full_text	String	User-provided location string from profile
members[].location.lat	Float	Geocoded latitude coordinate (when resolvable)
members[].location.lng	Float	Geocoded longitude coordinate (when resolvable)
members[].location.country_code	String	ISO 3166-1 alpha-2 country code derived from location
members[].engagement_metrics.avg_likes	Integer	Average number of likes per tweet (calculated from recent activity)
members[].engagement_metrics.avg_retweets	Integer	Average number of retweets per tweet (calculated from recent activity)
members[].engagement_metrics.avg_replies	Integer	Average number of replies per tweet (calculated from recent activity)
list_metadata.owner_id	String	Twitter user ID of the list creator/owner
list_metadata.created_at	String (ISO 8601)	UTC timestamp when the list was originally created
list_metadata.is_private	Boolean	Indicates whether the list is private or publicly accessible
list_metadata.description	String	User-provided description text for the list
list_metadata.category	String	Categorical classification of the list topic
extraction_info.version	String	Semantic version of the extraction actor
extraction_info.success_rate	Float	Ratio of successfully extracted profiles to total attempted (0.0-1.0)
extraction_info.rate_limited	Boolean	Flag indicating if rate limiting was encountered during extraction
extraction_info.processing_time_ms	Integer	Total processing duration in milliseconds
sentiment_score	Float	Aggregate sentiment score across member profiles (-1.0 to 1.0)
last_updated	String (ISO 8601)	UTC timestamp of the most recent data refresh
is_complete	Boolean	Indicates whether all list members were successfully extracted
total_pages_scraped	Integer	Number of pagination cycles completed during extraction

Sample Dataset

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

{
  "list_id": "1234567890123456789",
  "member_count": 342,
  "scraped_at": "2025-12-21T15:30:22Z",
  "external_id": "tw_list_mem_98765432112345",
  "members": [
    {
      "user_id": "87654321",
      "screen_name": "tech_analyst",
      "display_name": "Tech Insights",
      "is_verified": true,
      "language_code": "en",
      "followers_count": 45231,
      "location": {
        "full_text": "San Francisco, CA",
        "lat": 37.7749,
        "lng": -122.4194,
        "country_code": "US"
      },
      "engagement_metrics": {
        "avg_likes": 892,
        "avg_retweets": 156,
        "avg_replies": 34
      }
    }
  ],
  "list_metadata": {
    "owner_id": "76543210",
    "created_at": "2024-06-15T08:22:31Z",
    "is_private": false,
    "description": "Tech industry analysts and thought leaders",
    "category": "Technology"
  },
  "extraction_info": {
    "version": "2.1.4",
    "success_rate": 0.98,
    "rate_limited": false,
    "processing_time_ms": 1234
  },
  "sentiment_score": 0.75,
  "last_updated": "2025-12-21T15:30:22Z",
  "is_complete": true,
  "total_pages_scraped": 4
}

Configuration Parameters

To ensure optimal data depth, configure the following:

Parameter	JSON Field Name	Data Type	Example Value	Description
List ID	listId	String	"1177128103228989440"	The unique identifier of the Twitter list to extract. Can be obtained from the list URL.

Analytical Use Cases

Lead Scoring & Qualification: Sales intelligence teams leverage follower counts, verification status, and engagement metrics to prioritize high-value prospects and filter out low-engagement accounts from outreach campaigns.

Geographic Market Segmentation: Location data enables territory-based lead assignment and regional market analysis, allowing sales teams to identify geographic clusters of decision-makers within specific industries.

Influencer Identification: Engagement metrics (avg_likes, avg_retweets) combined with follower counts provide quantitative signals for identifying industry thought leaders and potential brand advocates.

Competitive Intelligence: By analyzing lists curated by competitors or industry organizations, researchers can map competitive landscapes, identify emerging players, and track shifts in market positioning.

Sentiment Analysis: Aggregate sentiment scores across list members provide macro-level indicators of industry mood, brand perception, or topic-specific attitudes within professional communities.

Network Mapping: User relationships within lists enable social graph construction for understanding influence patterns, information flow, and community structure within B2B ecosystems.

Longitudinal Studies: Time-series analysis of scraped_at and last_updated fields supports tracking of membership changes, follower growth trajectories, and engagement trend analysis over extended periods.

Technical Limitations

Rate Limiting: Twitter's platform enforces request throttling that may impact extraction speed for lists exceeding 500 members. The rate_limited flag in extraction_info indicates when delays were encountered.

Geolocation Accuracy: The location object relies on user-provided text and geocoding services. Approximately 30-40% of profiles lack structured location data, resulting in null values for lat, lng, and country_code fields.

Engagement Metric Sampling: Average engagement calculations (avg_likes, avg_retweets, avg_replies) are derived from the most recent 20 tweets per user and may not represent long-term engagement patterns.

Private List Access: Extraction is limited to public lists only. Private lists return empty member arrays regardless of authentication status due to cookieless architecture.

Data Freshness: Member profiles reflect point-in-time snapshots. Follower counts and verification status may change between extraction and analysis. The scraped_at timestamp provides temporal context for data currency assessment.

Pagination Constraints: Lists exceeding 5,000 members may experience incomplete extraction due to platform-imposed pagination limits. The is_complete boolean flag indicates extraction completeness.

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Keywords & Tags: This web scraping tool functions as a specialized data extractor for Twitter list intelligence, complementing linkedin scraper and instagram scraper workflows. Capabilities include export followers data, extract emails from profile metadata, and accelerate lead generation through web data extraction pipelines optimized for B2B prospecting.