Twitter (X) Post Comments Extractor (Rich Metadata) cookieless

"# Twitter (X.com) Post Comments Extractor (Rich Metadata) cookieless

Overview

This actor performs a deep extraction of comment-level data from Twitter (X.com) posts, capturing rich metadata including engagement metrics, author attributes, sentiment indicators, and temporal information. The extraction process operates without authentication cookies, ensuring reliable data collection with high fidelity schema adherence. All records include comprehensive timestamp tracking and nested object structures for author profiles, engagement statistics, and platform metadata to support downstream analytical workflows.

Data Dictionary

Field Name	Data Type	Definition
post_id	String	Unique identifier for the parent post to which the comment belongs
external_id	String	Platform-generated unique identifier for the individual comment record
scraped_at	String (ISO 8601)	UTC timestamp indicating when the comment data was extracted from the platform
parent_thread_id	String	Identifier for the conversation thread containing this comment
content	String	Full text content of the comment as posted by the user
language_code	String	ISO 639-1 two-letter language code detected for the comment content
sentiment_score	Float	Normalized sentiment polarity score ranging from -1.0 (negative) to 1.0 (positive)
is_verified	Boolean	Indicates whether the comment author has platform verification status
is_edited	Boolean	Flag indicating if the comment has been modified after initial posting
comment_depth	Integer	Nesting level of the comment within the thread hierarchy (0 = top-level)
author.user_id	String	Unique platform identifier for the comment author
author.username	String	Display username or handle of the comment author
author.reputation_score	Integer	Numerical representation of author's platform credibility or karma
author.is_premium	Boolean	Indicates whether the author holds a premium or subscription account status
engagement_metrics.upvotes	Integer	Count of positive reactions or likes received by the comment
engagement_metrics.downvotes	Integer	Count of negative reactions received by the comment
engagement_metrics.replies	Integer	Number of direct replies to this comment
engagement_metrics.shares	Integer	Count of times the comment has been shared or reposted
metadata.client_version	String	Version identifier of the client application used to post the comment
metadata.platform	String	Platform interface type (web, mobile_app, api)
metadata.device_type	String	Device category from which the comment was posted (desktop, mobile, tablet)
metadata.ip_region	String	Geographic region code derived from the posting IP address
timestamps.created_at	String (ISO 8601)	UTC timestamp of the original comment creation
timestamps.last_edited	String (ISO 8601)	UTC timestamp of the most recent edit operation
timestamps.last_activity	String (ISO 8601)	UTC timestamp of the most recent engagement activity on the comment

Sample Dataset

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

{
  ""post_id"": ""p_87654321"",
  ""external_id"": ""comment_15f8a9c2d3b4e5"",
  ""scraped_at"": ""2025-12-21T15:22:33Z"",
  ""parent_thread_id"": ""thread_98765432"",
  ""content"": ""This analysis perfectly captures the market trends!"",
  ""language_code"": ""en"",
  ""sentiment_score"": 0.87,
  ""is_verified"": true,
  ""is_edited"": false,
  ""comment_depth"": 2,
  ""author"": {
    ""user_id"": ""u_123456789"",
    ""username"": ""market_analyst"",
    ""reputation_score"": 856,
    ""is_premium"": true
  },
  ""engagement_metrics"": {
    ""upvotes"": 234,
    ""downvotes"": 12,
    ""replies"": 15,
    ""shares"": 8
  },
  ""metadata"": {
    ""client_version"": ""2.14.5"",
    ""platform"": ""web"",
    ""device_type"": ""desktop"",
    ""ip_region"": ""EUR""
  },
  ""timestamps"": {
    ""created_at"": ""2025-12-21T14:30:00Z"",
    ""last_edited"": ""2025-12-21T14:45:22Z"",
    ""last_activity"": ""2025-12-21T15:20:11Z""
  }
}

Configuration Parameters

To ensure optimal data depth, configure the following:

Parameter	JSON Field Name	Data Type	Required	Description	Example Value
Tweet ID	tweetId	String	Yes	The unique identifier of the Twitter post from which to extract comments	""1738106896777699464""

Analytical Use Cases

Researchers and data scientists can leverage this dataset for multiple analytical workflows:

• Sentiment Analysis: Utilize the sentiment_score field in conjunction with content to perform aggregate sentiment tracking across comment threads, enabling identification of positive/negative discourse patterns.
• Engagement Pattern Analysis: Correlate engagement_metrics with author.reputation_score and timestamps to identify high-performing comment characteristics and optimal posting windows.
• Network Mapping: Construct social graphs using author.user_id, parent_thread_id, and comment_depth to visualize conversation hierarchies and identify influential community members.
• Longitudinal Studies: Track temporal evolution of discussions using timestamps.created_at and timestamps.last_activity to measure conversation decay rates and sustained engagement periods.
• Content Strategy Optimization: Analyze language_code, content length, and engagement_metrics to determine which comment styles drive higher interaction rates.
• Verification Impact Studies: Compare engagement patterns between verified (is_verified: true) and non-verified authors to quantify credibility effects on audience response.

Technical Limitations

Important Considerations:

• Rate Limiting: Extraction operates within platform-imposed rate limits; high-volume requests may experience throttling or temporary access restrictions.
• Data Freshness: The scraped_at timestamp reflects extraction time; rapidly evolving threads may show discrepancies between engagement_metrics and real-time values.
• Deleted Content: Comments removed after extraction will not be retroactively flagged; implement periodic re-scraping for data integrity validation.
• Nested Thread Depth: Extremely deep comment threads (depth > 10) may experience incomplete extraction due to platform pagination constraints.
• Sentiment Accuracy: The sentiment_score represents algorithmic inference and may not capture nuanced sarcasm, cultural context, or domain-specific terminology.
• Geographic Precision: The ip_region field provides regional-level granularity only; city or precise location data is not available.
• Historical Data: Extraction is limited to currently accessible comments; platform retention policies may restrict access to older content beyond 12-18 months.

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Keywords & Tags: This specification supports workflows involving post comments scraper, social media comments extractor, instagram post comments scraping, facebook post comments scraper, export comments from posts, comment data scraping tool, and social engagement analytics for comprehensive audience insight generation."