Instagram Comments Extractor (Rich Metadata)

Overview

This actor performs a deep extraction of Instagram comment data with enriched metadata fields, including sentiment analysis, toxicity metrics, and author profile attributes. The extraction pipeline ensures data integrity through timestamp validation, nested object preservation, and comprehensive field coverage. Designed for high-reliability analytical workflows, this tool captures both surface-level engagement metrics and underlying behavioral signals critical for social media intelligence operations.

Data Dictionary

Field Name	Data Type	Definition
comment_id	String	Unique identifier assigned by Instagram to the comment object
parent_post_id	String	Identifier of the Instagram post to which this comment belongs
text	String	Raw comment content as authored by the user
created_at	String (ISO 8601)	Timestamp indicating when the comment was originally published on Instagram
scraped_at	String (ISO 8601)	Timestamp indicating when the comment was extracted by this actor
external_id	String	Alternative identifier used for cross-platform tracking or internal reference
language_code	String	ISO 639-1 two-letter language code detected from comment text
sentiment_score	Float	Normalized sentiment polarity score ranging from -1.0 (negative) to 1.0 (positive)
author.id	String	Unique identifier for the comment author's Instagram account
author.username	String	Public username handle of the comment author
author.is_verified	Boolean	Indicates whether the author account has Instagram verification status (blue check)
author.follower_count	Integer	Total number of followers associated with the author's account at time of extraction
author.profile_type	String	Classification of account type (e.g., personal, professional, business, creator)
engagement.likes	Integer	Total number of likes received by the comment
engagement.replies	Integer	Count of direct replies to this comment
engagement.reply_to_count	Integer	Number of times this comment has been replied to in nested threads
metadata.client_id	String	Device or client identifier from which the comment was posted
metadata.ip_region	String	Geographic region code derived from IP address at time of posting
metadata.is_edited	Boolean	Indicates whether the comment has been modified after initial publication
metadata.edit_history_count	Integer	Number of times the comment has been edited
toxicity_metrics.spam_probability	Float	Probability score (0.0-1.0) indicating likelihood of spam content
toxicity_metrics.hate_speech_score	Float	Probability score (0.0-1.0) indicating presence of hate speech or offensive language
toxicity_metrics.automated_flag	Boolean	Indicates whether the comment was flagged as potentially bot-generated
is_hidden	Boolean	Indicates whether the comment is hidden from public view by moderation or author action
has_mentions	Boolean	Indicates whether the comment contains @mentions of other users
mention_count	Integer	Total number of user mentions present in the comment text
is_pinned	Boolean	Indicates whether the comment has been pinned by the post author

Sample Dataset

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

{
  "comment_id": "18234567891234567",
  "parent_post_id": "25678901234567890",
  "text": "This analysis is spot on! The correlation between user engagement and time of posting is fascinating.",
  "created_at": "2025-12-19T08:15:23Z",
  "scraped_at": "2025-12-19T10:30:45Z",
  "external_id": "c_789012345678901234567890",
  "language_code": "en",
  "sentiment_score": 0.87,
  "author": {
    "id": "user_456789012",
    "username": "data_analyst_pro",
    "is_verified": true,
    "follower_count": 12453,
    "profile_type": "professional"
  },
  "engagement": {
    "likes": 234,
    "replies": 15,
    "reply_to_count": 3
  },
  "metadata": {
    "client_id": "IG_ANDROID_1234",
    "ip_region": "EUR",
    "is_edited": false,
    "edit_history_count": 0
  },
  "toxicity_metrics": {
    "spam_probability": 0.02,
    "hate_speech_score": 0.01,
    "automated_flag": false
  },
  "is_hidden": false,
  "has_mentions": true,
  "mention_count": 2,
  "is_pinned": false
}

Configuration Parameters

To ensure optimal data depth, configure the following:

Parameter	Field Name	Data Type	Required	Description
Post Identifier	postCode	String	Yes	Instagram post code, numeric ID, or full URL from which to extract comments

Accepted Input Formats:

• Short code: CXa1b2c3D4e
• Numeric ID: 25678901234567890
• Full URL: https://www.instagram.com/p/CXa1b2c3D4e/

Analytical Use Cases

Sentiment Analysis: Leverage sentiment_score and toxicity_metrics fields to quantify audience emotional response patterns across campaign content, enabling data-driven adjustments to messaging strategy.

Influencer Vetting: Cross-reference author.is_verified, author.follower_count, and author.profile_type to identify high-value commenters and potential brand advocates within target demographics.

Engagement Forecasting: Utilize temporal fields (created_at, scraped_at) alongside engagement metrics to model comment velocity curves and predict viral trajectory of content.

Network Mapping: Extract has_mentions and mention_count data to construct social graphs illustrating user interaction patterns and community structure within comment threads.

Content Moderation Intelligence: Apply toxicity_metrics and is_hidden flags to audit moderation effectiveness and identify emerging patterns in problematic content.

Longitudinal Studies: Track edit_history_count and is_edited fields to analyze comment revision behavior and measure shifts in public discourse over time.

Technical Limitations

Important Considerations:

• Rate Limiting: Instagram enforces dynamic rate limits on comment extraction. Expect throttling after approximately 200-500 comments per hour depending on account status and IP reputation.
• Data Freshness: The scraped_at timestamp reflects extraction time; comments may be modified or deleted after capture, creating temporal inconsistencies in longitudinal datasets.
• Private Accounts: Comments from private accounts are not accessible unless the extraction is performed by an authenticated follower.
• Nested Thread Depth: Reply extraction is limited to 3 levels of nesting; deeply nested conversations may be truncated.
• Sentiment Accuracy: The sentiment_score field uses heuristic NLP models with approximately 78-82% accuracy on informal social media text; manual validation recommended for critical analyses.
• Deleted Content: Comments deleted between initial posting and extraction will not appear in the dataset, potentially introducing survivorship bias.
• Metadata Availability: Fields within metadata and toxicity_metrics objects depend on Instagram's API response completeness and may occasionally return null values.

---

Keywords & Tags: This specification supports workflows involving instagram scraper, instagram comments scraper, instagram comment scraping, export instagram comments, instagram data extractor, instagram analytics scraping, and instagram comment mining operations for social media intelligence and competitive analysis.