Twitter (X) User Tweets Extractor (Rich Metadata) cookieless

"# Twitter (X.com) User Tweets Extractor (Rich Metadata) cookieless

Overview

This actor performs a deep extraction of tweet-level data from specified Twitter user timelines, capturing comprehensive metadata including engagement metrics, author profiles, entity annotations, and geolocation information. The extraction process operates without authentication cookies, ensuring reliable data collection with high fidelity schema adherence. All timestamps are normalized to ISO 8601 format, and nested objects maintain referential integrity for downstream analytical workflows.

Data Dictionary

Field Name	Data Type	Definition
tweet_id	String	Unique identifier assigned by Twitter to the tweet object
external_id	String	Internal tracking identifier prefixed with 'tw_' for cross-system reconciliation
created_at	String (ISO 8601)	UTC timestamp indicating when the tweet was originally published
scraped_at	String (ISO 8601)	UTC timestamp recording when the extraction process captured this record
text	String	Full UTF-8 encoded tweet content including hashtags and mentions
language_code	String	ISO 639-1 two-letter language code detected by Twitter's classification system
is_retweet	Boolean	Flag indicating whether this tweet is a retweet of another user's content
is_quote	Boolean	Flag indicating whether this tweet quotes another tweet with added commentary
author.user_id	String	Unique numeric identifier for the tweet author's account
author.username	String	Twitter handle (without @ symbol) of the account that published the tweet
author.display_name	String	Human-readable name displayed on the author's profile
author.is_verified	Boolean	Indicates whether the account has Twitter verification status (blue checkmark)
author.follower_count	Integer	Number of accounts following this user at time of extraction
author.following_count	Integer	Number of accounts this user follows at time of extraction
metrics.retweet_count	Integer	Cumulative number of times this tweet has been retweeted
metrics.like_count	Integer	Cumulative number of likes (favorites) received by this tweet
metrics.reply_count	Integer	Number of direct replies to this tweet
metrics.quote_count	Integer	Number of quote tweets referencing this original tweet
entities.hashtags	Array[String]	List of hashtag strings extracted from tweet text (without # symbol)
entities.mentions	Array[String]	List of @username mentions contained within the tweet
entities.urls	Array[String]	List of expanded URLs shared in the tweet content
sentiment_score	Float	Normalized sentiment polarity score ranging from -1.0 (negative) to 1.0 (positive)
device_source	String	Client application or platform used to publish the tweet
geo.coordinates.lat	Float	Latitude coordinate if geolocation data is attached to the tweet
geo.coordinates.lng	Float	Longitude coordinate if geolocation data is attached to the tweet
geo.place_id	String	Twitter Place ID referencing a named location entity
conversation_id	String	Identifier linking this tweet to its parent conversation thread

Sample Dataset

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

{
  ""tweet_id"": ""1472891234567890123"",
  ""external_id"": ""tw_15f2e8b9c7d6a4"",
  ""created_at"": ""2025-12-21T15:30:22Z"",
  ""scraped_at"": ""2025-12-21T16:00:00Z"",
  ""text"": ""Just shared our latest findings on renewable energy adoption rates #sustainability"",
  ""language_code"": ""en"",
  ""is_retweet"": false,
  ""is_quote"": false,
  ""author"": {
    ""user_id"": ""283749162"",
    ""username"": ""green_tech_news"",
    ""display_name"": ""Green Tech Daily"",
    ""is_verified"": true,
    ""follower_count"": 52891,
    ""following_count"": 1234
  },
  ""metrics"": {
    ""retweet_count"": 342,
    ""like_count"": 1205,
    ""reply_count"": 89,
    ""quote_count"": 27
  },
  ""entities"": {
    ""hashtags"": [""sustainability""],
    ""mentions"": [],
    ""urls"": []
  },
  ""sentiment_score"": 0.78,
  ""device_source"": ""Twitter Web App"",
  ""geo"": {
    ""coordinates"": {
      ""lat"": 40.7128,
      ""lng"": -74.0060
    },
    ""place_id"": ""01a9a39c27f5cb71""
  },
  ""conversation_id"": ""1472891234567890123""
}

Configuration Parameters

To ensure optimal data depth, configure the following:

Parameter	JSON Field Name	Data Type	Required	Description	Example Value
Username	userId	String	Yes	Twitter handle of the target account (without @ symbol)	elonmusk

Analytical Use Cases

Sentiment Analysis: Leverage the sentiment_score field alongside text content to perform time-series sentiment tracking across brand mentions or topic clusters. Aggregate sentiment by date ranges to identify reputation trends.

Engagement Pattern Analysis: Utilize metrics object fields (retweet_count, like_count, reply_count, quote_count) to calculate engagement rates, identify viral content thresholds, and benchmark performance against follower base size.

Network Mapping: Extract entities.mentions arrays to construct directed graphs of user interactions, identifying key influencers and community clusters within specific discourse networks.

Temporal Content Strategy: Analyze created_at timestamps in conjunction with engagement metrics to determine optimal posting schedules and content lifecycle patterns for audience segments.

Geospatial Audience Profiling: When geo data is present, map tweet origins to understand geographic distribution of engaged audiences and regional sentiment variations.

Longitudinal Studies: Track changes in author.follower_count and author.is_verified status over multiple extraction runs to monitor account growth trajectories and verification events.

Technical Limitations

Important Considerations:

• Extraction operates within Twitter's public data access boundaries; protected accounts and deleted tweets are not retrievable
• The geo object will contain null values for tweets without location data enabled by the author
• Sentiment scores are algorithmically generated and should be validated against domain-specific lexicons for specialized industries
• Historical tweet availability may be limited to the most recent 3,200 tweets per user timeline due to platform API constraints
• Rate limiting may affect extraction velocity; recommended batch size is 200 tweets per request cycle
• The scraped_at timestamp reflects extraction time, not data freshness; compare with created_at to assess temporal lag
• Engagement metrics (metrics object) represent point-in-time snapshots and will not auto-update as tweets continue to accumulate interactions
• Cookieless operation ensures compliance but may result in reduced access to certain premium metadata fields available through authenticated endpoints

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Keywords & Tags: This specification supports workflows involving twitter scraper, twitter user tweets, extract tweets from users, export tweets, tweet scraper API, twitter data extraction, and lead generation from tweets for social media intelligence applications."