Twitter(X) Trends Location Extractor (Rich Metadata) cookieless

"# Twitter (X.com) Trends By Location Extractor (Rich Metadata) cookieless

Overview

This actor performs a deep extraction of location-specific trending topics from Twitter (X.com), capturing rich metadata including engagement metrics, sentiment analysis, and temporal dynamics. The extractor operates without authentication requirements, ensuring reliable data collection with high fidelity schema compliance. All outputs include data quality indicators and processing metadata to support rigorous analytical workflows and reproducible research.

Data Dictionary

Field Name	Data Type	Definition
request_id	String	Unique identifier for the extraction request, used for tracking and audit trails
scraped_at	String (ISO 8601)	UTC timestamp indicating when the data extraction was performed
external_id	String	External reference identifier linking to the source trend object on Twitter's platform
location.city	String	City name where the trending topic is geographically concentrated
location.region	String	State or regional administrative division for the trending location
location.country	String	ISO country code or full country name for the trending location
location.coordinates.lat	Float	Latitude coordinate of the location centroid
location.coordinates.lng	Float	Longitude coordinate of the location centroid
location.timezone	String	IANA timezone identifier for the location (e.g., America/Los_Angeles)
trends.volume	Integer	Total volume of mentions or interactions associated with the trending topic
trends.momentum_score	Float	Normalized score (0-1) indicating the acceleration rate of trend growth
trends.peak_time	String (ISO 8601)	Timestamp when the trend reached maximum velocity or volume
trends.velocity	Float	Rate of change in trend volume per unit time (mentions per hour)
metadata.source_type	String	Classification of data aggregation method (e.g., aggregated, sampled, real-time)
metadata.confidence_score	Float	Statistical confidence level (0-1) in the accuracy of the extracted data
metadata.language_code	String	BCP 47 language tag indicating the primary language of the trending content
metadata.is_verified	Boolean	Indicates whether the trend has been validated against Twitter's official trending data
metadata.data_quality	Float	Composite quality score (0-1) based on completeness, consistency, and freshness
engagement_metrics.total_mentions	Integer	Aggregate count of all mentions, posts, and references to the trending topic
engagement_metrics.unique_users	Integer	Count of distinct user accounts participating in the trend
engagement_metrics.sentiment_score	Float	Normalized sentiment polarity score (-1 to 1, where 1 is positive)
engagement_metrics.share_of_voice	Float	Proportional representation (0-1) of this trend relative to all trending topics
processing_info.algorithm_version	String	Semantic version identifier of the extraction and processing algorithm
processing_info.sample_size	Integer	Number of data points analyzed to generate the trend statistics
processing_info.margin_error	Float	Statistical margin of error for volume and engagement estimates

Sample Dataset

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

{
  ""request_id"": ""loc_trends_25121908"",
  ""scraped_at"": ""2025-12-21T12:00:00Z"",
  ""external_id"": ""gt_7891234567890123"",
  ""location"": {
    ""city"": ""San Francisco"",
    ""region"": ""California"",
    ""country"": ""US"",
    ""coordinates"": {
      ""lat"": 37.7749,
      ""lng"": -122.4194
    },
    ""timezone"": ""America/Los_Angeles""
  },
  ""trends"": {
    ""volume"": 45678,
    ""momentum_score"": 0.87,
    ""peak_time"": ""2025-12-21T08:30:00Z"",
    ""velocity"": 234.5
  },
  ""metadata"": {
    ""source_type"": ""aggregated"",
    ""confidence_score"": 0.95,
    ""language_code"": ""en-US"",
    ""is_verified"": true,
    ""data_quality"": 0.89
  },
  ""engagement_metrics"": {
    ""total_mentions"": 12567,
    ""unique_users"": 8901,
    ""sentiment_score"": 0.76,
    ""share_of_voice"": 0.34
  },
  ""processing_info"": {
    ""algorithm_version"": ""4.2.1"",
    ""sample_size"": 25000,
    ""margin_error"": 0.02
  }
}

Configuration Parameters

To ensure optimal data depth, configure the following:

Parameter	JSON Field Name	Data Type	Required	Example	Description
Country	country	String	Yes	""India""	Target country for location-specific trend extraction. Accepts ISO country codes or full country names.

Analytical Use Cases

Researchers and analysts can leverage this dataset for multiple analytical workflows:

• Sentiment Analysis: Utilize sentiment_score and engagement_metrics to perform temporal sentiment tracking and identify shifts in public opinion across geographic regions.
• Geographic Trend Mapping: Combine location.coordinates with trends.volume to create heat maps visualizing trending topic distribution and regional concentration patterns.
• Momentum Forecasting: Apply time-series analysis to momentum_score and velocity fields to predict trend lifecycle and identify emerging topics before peak saturation.
• Comparative Market Intelligence: Leverage share_of_voice metrics to benchmark competitive positioning and identify market gaps in location-specific conversations.
• Longitudinal Studies: Track request_id and scraped_at timestamps to build historical trend databases for studying seasonal patterns and long-term topic evolution.
• Data Quality Auditing: Use confidence_score, data_quality, and margin_error fields to filter datasets and ensure statistical rigor in downstream analyses.

Technical Limitations

Important Considerations:

• Sampling Methodology: Trend statistics are derived from sample populations indicated by processing_info.sample_size. Actual platform-wide volumes may vary beyond the stated margin_error.
• Temporal Granularity: Trend data reflects point-in-time snapshots at scraped_at. Rapid trend evolution may occur between extraction intervals.
• Geographic Precision: Location data represents administrative boundaries and centroids. Actual trend distribution may extend beyond specified coordinates.
• Authentication-Free Constraints: Cookieless extraction may have reduced access to certain metadata fields compared to authenticated API endpoints.
• Rate Limiting: Extraction frequency is subject to platform-imposed rate limits. High-volume requests may require throttling or distributed execution.
• Data Retention: Historical trend data availability depends on platform retention policies. Trends older than 7 days may have reduced metadata completeness.
• Language Detection: language_code is algorithmically inferred and may not reflect multilingual content or code-switching within trending topics.

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Keywords & Tags: This web scraper and data extractor tool enables analysts to export data from websites, specifically functioning as a social media scraper for Twitter trends. The website scraper supports lead generation scraper workflows and can be integrated with ecommerce product scraper pipelines for comprehensive market intelligence."