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: json { "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.