Instagram Hashtags Extractor (Rich Metadata)

Overview

This actor performs a deep extraction of Instagram hashtag metadata, delivering structured analytics on engagement patterns, geographic distribution, sentiment indicators, and temporal trends. The extraction pipeline ensures data integrity through timestamp verification, external ID tracking, and multi-dimensional validation. Designed for high-fidelity data operations, this tool provides reliable, schema-consistent outputs suitable for downstream analytical workflows and machine learning pipelines.

Data Dictionary

Field Name	Data Type	Definition
search_id	String	Unique identifier for the extraction session, prefixed with hs_
hashtag	String	The target hashtag string queried during extraction
total_posts	Integer	Cumulative count of posts associated with the hashtag at extraction time
scraped_at	String (ISO 8601)	UTC timestamp indicating when the data extraction was executed
external_id	String	External reference identifier for cross-system tracking, prefixed with hscrp_
language_code	String	ISO 639-1 two-letter language code representing primary content language
is_trending	Boolean	Indicator of whether the hashtag is currently classified as trending
daily_volume	Integer	Estimated number of new posts using this hashtag per 24-hour period
metrics.avg_engagement_rate	Float	Mean engagement rate (likes + comments / followers) expressed as percentage
metrics.reach_score	Float	Proprietary metric (0-100) estimating potential audience reach
metrics.growth_velocity	Float	Rate of hashtag adoption growth, expressed as multiplier over baseline
top_locations.cities	Array[String]	Top three cities by post volume associated with this hashtag
top_locations.countries	Array[String]	ISO 3166-1 alpha-2 country codes for top three countries by usage
top_locations.coordinates.lat	Float	Latitude coordinate of primary geographic centroid
top_locations.coordinates.lng	Float	Longitude coordinate of primary geographic centroid
usage_stats.business_accounts	Float	Percentage of posts from business/creator accounts
usage_stats.personal_accounts	Float	Percentage of posts from personal accounts
usage_stats.verified_ratio	Float	Ratio of verified accounts using this hashtag (0-1 scale)
sentiment_analysis.score	Float	Aggregate sentiment score ranging from -1 (negative) to +1 (positive)
sentiment_analysis.positive_mentions	Integer	Count of posts with positive sentiment indicators
sentiment_analysis.negative_mentions	Integer	Count of posts with negative sentiment indicators
related_hashtags	Array[String]	Co-occurring hashtags frequently used alongside the target hashtag
peak_hours	Array[Integer]	Hours of day (0-23, UTC) with highest posting activity
content_categories	Array[String]	Classified content themes associated with hashtag usage
verification_status.is_verified	Boolean	Indicates if the hashtag data has passed quality validation checks
verification_status.verified_date	String (ISO 8601)	UTC timestamp of when data verification was completed

Sample Dataset

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

{
  "search_id": "hs_2025121934982",
  "hashtag": "instagram-search-hashtags",
  "total_posts": 145892,
  "scraped_at": "2025-12-19T14:22:31Z",
  "external_id": "hscrp_8f29a4d7e6b3c2a1",
  "language_code": "en",
  "is_trending": true,
  "daily_volume": 2341,
  "metrics": {
    "avg_engagement_rate": 3.2,
    "reach_score": 78.5,
    "growth_velocity": 1.4
  },
  "top_locations": {
    "cities": ["New York", "London", "Mumbai"],
    "countries": ["US", "UK", "IN"],
    "coordinates": {
      "lat": 40.7128,
      "lng": -74.0060
    }
  },
  "usage_stats": {
    "business_accounts": 45.2,
    "personal_accounts": 54.8,
    "verified_ratio": 0.12
  },
  "sentiment_analysis": {
    "score": 0.67,
    "positive_mentions": 823,
    "negative_mentions": 156
  },
  "related_hashtags": ["socialmedia", "digitalmarketing", "instagramtips"],
  "peak_hours": [13, 15, 19],
  "content_categories": ["marketing", "business", "technology"],
  "verification_status": {
    "is_verified": true,
    "verified_date": "2025-11-30T00:00:00Z"
  }
}

Configuration Parameters

To ensure optimal data depth, configure the following:

Parameter	Field Name	Data Type	Required	Description	Example
Search Term	query	String	Yes	Hashtag keyword or search term to extract (without # prefix)	insights

Analytical Use Cases

Sentiment Analysis: Leverage sentiment_analysis object to perform time-series sentiment tracking across marketing campaigns, identifying shifts in audience perception and brand health indicators.

Geographic Network Mapping: Utilize top_locations data to construct spatial distribution models, enabling region-specific content strategies and localized campaign optimization.

Temporal Pattern Recognition: Apply peak_hours and daily_volume metrics to build predictive models for optimal posting schedules and content calendar planning.

Competitive Intelligence: Cross-reference related_hashtags and content_categories to map competitive landscape positioning and identify emerging content opportunities.

Longitudinal Studies: Track growth_velocity and is_trending flags over multiple extraction sessions to measure hashtag lifecycle stages and predict virality trajectories.

Audience Segmentation: Analyze usage_stats to differentiate B2B versus B2C engagement patterns and tailor content strategies for business versus personal account audiences.

Technical Limitations

Important Considerations:

• Data extraction is subject to Instagram's rate limiting policies; sustained high-volume queries may result in temporary access restrictions
• total_posts represents a point-in-time snapshot and may not reflect real-time counts due to caching mechanisms
• Geographic coordinates represent centroid calculations and should not be interpreted as precise user locations
• Sentiment analysis scores are derived from NLP models with approximately 82% accuracy on English-language content; non-English content may exhibit reduced precision
• verification_status reflects data quality checks at extraction time; downstream validation is recommended for mission-critical applications
• Historical data retention is limited to 90 days; longitudinal studies requiring longer timeframes should implement external archival strategies
• related_hashtags array is limited to top 10 co-occurring tags by frequency
• API response times may vary between 2-8 seconds depending on hashtag popularity and platform load

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