$0.97/1000
results
Unpack any public X post — root tweet, threaded replies, and quote tweets in a single structured export. Configurable depth and caps. $0.97 per 1,000 results.
Unpack any public X post — root tweet, threaded replies, and quote tweets in a single structured export. Configurable depth and caps. $0.97 per 1,000 results.
Start from a tweet URL or numeric ID — optionally expand into replies and quote tweets with conversation depth preserved.
On Scrapy.io, X Tweet Data Extractor by Thunderbolt is built for how to scrape information from a website workflows — run from the Store, schedule recurring jobs, or POST to thunderbolt.p.scrapy.io from your stack.
tweetsinclude_replies and include_quotes with separate capsX (Twitter) remains a real-time signal for news, brand sentiment, and creator distribution. A how to scrape information from a website job on Scrapy.io hydrates handles and queries into JSON your warehouse can diff over time.
X Tweet Data Extractor is built for repeatable extraction with caps you control — the same fast, API-first model Thunderbolt is known for on other platforms.
This how to scrape information from a website on Scrapy.io exports structured fields including:
For your first run, keep caps low (maxResults, maxReviews, etc.) until you validate row shape in the Output preview.
Teams discover X Tweet Data Extractor while researching how to scrape information from a website and related terms such as google scraping, apify twitter scraper, map scraping, python twitter scraper. This listing is structured to answer common questions — including — with concrete Input and Output examples you can run on Scrapy.io without maintaining your own scraper infrastructure.
If you arrived from SEO or paid search, start with the Input JSON example below, run once on the free tier, then scale with schedules or the publisher API once row shape and cost look right.
Use this checklist before your first production run of X Tweet Data Extractor:
maxResults, maxReviews, or equivalent limits so a full batch stays within budgetthunderbolt.p.scrapy.io/x-tweet-scraper/v1/api or create a scheduled jobMost teams keep a "golden" input file in git for X Tweet Data Extractor so staging and production use identical payloads. Update that file when you add keywords, targets, or communities — not the orchestration code around it.
Paste tweet URLs or IDs. Optionally expand replies, quotes, favoriters, and retweeters.
Configure the run in the Scrapy.io input form or pass JSON to the publisher API.
{
"tweets": [
"example"
]
}| Field | Type | Required | Description |
|---|---|---|---|
tweets | string[] | Yes | One public tweet URL (x.com/.../status/... or twitter.com/.../status/...) or numeric tweet ID per line |
Results appear in the Scrapy.io run Output tab. Tweets, Replies, Quotes, Favoriters, Retweeters, and Errors views.
Each successful row is designed for warehouse loads: stable column names, explicit error rows, and caps you control in the input form.
{
"input": "https://x.com/example/status/1234567890",
"include_replies": true,
"include_quotes": true,
"max_replies": 50
}Download the full dataset as JSON, CSV, or Excel from the Output tab, or fetch results programmatically via the publisher API after the run completes.
capture how a statement spreads through replies
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
harvest quote tweets around a launch
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
rebuild conversation trees for qualitative coding
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
snapshot a viral thread with metadata intact
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
Dedicated conversation unpacker — root tweet plus replies and quotes in one billed run. Prefer Thunderbolt when thread depth matters, not just the root post.
X Tweet Data Extractor is rarely the only step in a data workflow. Teams typically:
See Related Thunderbolt tools below for same-publisher companions you can chain without leaving the Scrapy.io billing account.
Most production deployments follow the same shape:
postUrl, placeId, videoId, reviewId, …) plus scrape date where history mattersX Tweet Data Extractor separates successful rows from per-input errors, so staging logic can WHERE type != 'error' (or the equivalent filter for your tool) without failing the whole batch. Join across Thunderbolt tools on those stable keys to build multi-step datasets without re-scraping upstream steps.
Maintaining Facebook, LinkedIn, Instagram, or X extractors in-house means handling rate limits, HTML or API changes, proxy rotation, and observability — work that rarely differentiates your product. X Tweet Data Extractor on Scrapy.io outsources that operational burden: you supply inputs, Scrapy.io runs the job, and you pay per successful row.
Thunderbolt listings add API-first, warehouse-ready documentation (clear caps, field lists, and pipeline patterns) so data teams can onboard without reading executor source code. When requirements change, update the input JSON or schedule — not your scraper fleet.
X Tweet Data Extractor runs on Scrapy.io support multiple ways to get data out:
For production pipelines, most teams land JSON in object storage, normalize in dbt or Spark, and schedule recurring runs so the Store listing becomes just another data source — not a manual step.
Run X Tweet Data Extractor once from the Store to validate inputs, then promote the same JSON payload to a scheduled job on Scrapy.io. Common patterns:
thunderbolt.p.scrapy.io on a queueThunderbolt tools use pay-per-result billing, so scheduled jobs stay predictable when you cap maxResults, max_posts, max_comments, or equivalent limits in the input form.
curl -X POST "https://thunderbolt.p.scrapy.io/x-tweet-scraper/v1/api" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"tweets": ["https://x.com/nasa/status/1234567890123456789"],
"include_replies": true,
"max_replies": 100,
"include_quotes": true,
"max_quotes": 50
}'Bulk runs via the scraper endpoint:
curl -X POST "https://thunderbolt.p.scrapy.io/x-tweet-scraper/v1/scraper" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"tweets": ["https://x.com/nasa/status/1234567890123456789"],
"include_replies": true,
"max_replies": 100,
"include_quotes": true,
"max_quotes": 50
}'Open the API tab on the Store listing for Python, JavaScript, cURL, OpenAPI, and MCP snippets.
X Tweet Data Extractor is available through the Scrapy.io MCP server — configure your API key in Cursor, Claude Desktop, or another MCP client, then invoke the tool from agent workflows. This is useful for ad-hoc research ("scrape these ten URLs and summarize the Output") without writing boilerplate HTTP code.
For production, prefer the publisher API or scheduled runs; use MCP for exploratory analysis and internal tooling where a human or agent supplies inputs interactively. See the API tab on X Tweet Data Extractor for MCP connection snippets.
| Tool | Best for |
|---|---|
| LinkedIn Profile Lead Scraper | Thunderbolt companion tool — overview |
| Instagram Profile Bulk Extractor | Thunderbolt companion tool — overview |
| X Profile Insights Extractor | Thunderbolt companion tool — overview |
Disable quotes/replies when you only need the root tweet metrics
Batch URLs in API jobs with per-tweet error isolation
Store inputTweetId for joins across engagement exports
Inspect per-row errors in the Errors output view instead of failing an entire batch
Facebook-heavy runs failing in a pattern? Use Issues on this Thunderbolt listing and attach your run ID plus one post, page, or search query
X Tweet Data Extractor collects publicly available data only. You are responsible for complying with platform terms, applicable laws, and data-protection regulations (including GDPR when personal data is involved). Consult your legal team for your specific use case.
$0.40 per 1,000 results — billed per successful row on Scrapy.io. You are charged only for successfully scraped rows. See Scrapy.io pricing for plan details.
Yes. Use webhooks, scheduled runs, or the publisher API to push data into your warehouse, CRM, or automation stack.
Yes. POST to https://thunderbolt.p.scrapy.io/x-tweet-scraper/v1/api with your API key. See the API tab on the Store listing for ready-made snippets.
Yes. Configure the Scrapy.io MCP endpoint with your API key to query X Tweet Data Extractor from AI assistants and agent workflows.
Thunderbolt focuses on Meta and social discovery workflows — leave a Reviews note if this actor saved you from browser scraping and which Facebook surface you use daily.
Start from a tweet URL or numeric ID — optionally expand into replies and quote tweets with conversation depth preserved.
On Scrapy.io, X Tweet Data Extractor by Thunderbolt is built for how to scrape information from a website workflows — run from the Store, schedule recurring jobs, or POST to thunderbolt.p.scrapy.io from your stack.
tweetsinclude_replies and include_quotes with separate capsX (Twitter) remains a real-time signal for news, brand sentiment, and creator distribution. A how to scrape information from a website job on Scrapy.io hydrates handles and queries into JSON your warehouse can diff over time.
X Tweet Data Extractor is built for repeatable extraction with caps you control — the same fast, API-first model Thunderbolt is known for on other platforms.
This how to scrape information from a website on Scrapy.io exports structured fields including:
For your first run, keep caps low (maxResults, maxReviews, etc.) until you validate row shape in the Output preview.
Teams discover X Tweet Data Extractor while researching how to scrape information from a website and related terms such as google scraping, apify twitter scraper, map scraping, python twitter scraper. This listing is structured to answer common questions — including — with concrete Input and Output examples you can run on Scrapy.io without maintaining your own scraper infrastructure.
If you arrived from SEO or paid search, start with the Input JSON example below, run once on the free tier, then scale with schedules or the publisher API once row shape and cost look right.
Use this checklist before your first production run of X Tweet Data Extractor:
maxResults, maxReviews, or equivalent limits so a full batch stays within budgetthunderbolt.p.scrapy.io/x-tweet-scraper/v1/api or create a scheduled jobMost teams keep a "golden" input file in git for X Tweet Data Extractor so staging and production use identical payloads. Update that file when you add keywords, targets, or communities — not the orchestration code around it.
Paste tweet URLs or IDs. Optionally expand replies, quotes, favoriters, and retweeters.
Configure the run in the Scrapy.io input form or pass JSON to the publisher API.
{
"tweets": [
"example"
]
}| Field | Type | Required | Description |
|---|---|---|---|
tweets | string[] | Yes | One public tweet URL (x.com/.../status/... or twitter.com/.../status/...) or numeric tweet ID per line |
Results appear in the Scrapy.io run Output tab. Tweets, Replies, Quotes, Favoriters, Retweeters, and Errors views.
Each successful row is designed for warehouse loads: stable column names, explicit error rows, and caps you control in the input form.
{
"input": "https://x.com/example/status/1234567890",
"include_replies": true,
"include_quotes": true,
"max_replies": 50
}Download the full dataset as JSON, CSV, or Excel from the Output tab, or fetch results programmatically via the publisher API after the run completes.
capture how a statement spreads through replies
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
harvest quote tweets around a launch
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
rebuild conversation trees for qualitative coding
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
snapshot a viral thread with metadata intact
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Thunderbolt with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
Dedicated conversation unpacker — root tweet plus replies and quotes in one billed run. Prefer Thunderbolt when thread depth matters, not just the root post.
X Tweet Data Extractor is rarely the only step in a data workflow. Teams typically:
See Related Thunderbolt tools below for same-publisher companions you can chain without leaving the Scrapy.io billing account.
Most production deployments follow the same shape:
postUrl, placeId, videoId, reviewId, …) plus scrape date where history mattersX Tweet Data Extractor separates successful rows from per-input errors, so staging logic can WHERE type != 'error' (or the equivalent filter for your tool) without failing the whole batch. Join across Thunderbolt tools on those stable keys to build multi-step datasets without re-scraping upstream steps.
Maintaining Facebook, LinkedIn, Instagram, or X extractors in-house means handling rate limits, HTML or API changes, proxy rotation, and observability — work that rarely differentiates your product. X Tweet Data Extractor on Scrapy.io outsources that operational burden: you supply inputs, Scrapy.io runs the job, and you pay per successful row.
Thunderbolt listings add API-first, warehouse-ready documentation (clear caps, field lists, and pipeline patterns) so data teams can onboard without reading executor source code. When requirements change, update the input JSON or schedule — not your scraper fleet.
X Tweet Data Extractor runs on Scrapy.io support multiple ways to get data out:
For production pipelines, most teams land JSON in object storage, normalize in dbt or Spark, and schedule recurring runs so the Store listing becomes just another data source — not a manual step.
Run X Tweet Data Extractor once from the Store to validate inputs, then promote the same JSON payload to a scheduled job on Scrapy.io. Common patterns:
thunderbolt.p.scrapy.io on a queueThunderbolt tools use pay-per-result billing, so scheduled jobs stay predictable when you cap maxResults, max_posts, max_comments, or equivalent limits in the input form.
curl -X POST "https://thunderbolt.p.scrapy.io/x-tweet-scraper/v1/api" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"tweets": ["https://x.com/nasa/status/1234567890123456789"],
"include_replies": true,
"max_replies": 100,
"include_quotes": true,
"max_quotes": 50
}'Bulk runs via the scraper endpoint:
curl -X POST "https://thunderbolt.p.scrapy.io/x-tweet-scraper/v1/scraper" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"tweets": ["https://x.com/nasa/status/1234567890123456789"],
"include_replies": true,
"max_replies": 100,
"include_quotes": true,
"max_quotes": 50
}'Open the API tab on the Store listing for Python, JavaScript, cURL, OpenAPI, and MCP snippets.
X Tweet Data Extractor is available through the Scrapy.io MCP server — configure your API key in Cursor, Claude Desktop, or another MCP client, then invoke the tool from agent workflows. This is useful for ad-hoc research ("scrape these ten URLs and summarize the Output") without writing boilerplate HTTP code.
For production, prefer the publisher API or scheduled runs; use MCP for exploratory analysis and internal tooling where a human or agent supplies inputs interactively. See the API tab on X Tweet Data Extractor for MCP connection snippets.
| Tool | Best for |
|---|---|
| LinkedIn Profile Lead Scraper | Thunderbolt companion tool — overview |
| Instagram Profile Bulk Extractor | Thunderbolt companion tool — overview |
| X Profile Insights Extractor | Thunderbolt companion tool — overview |
Disable quotes/replies when you only need the root tweet metrics
Batch URLs in API jobs with per-tweet error isolation
Store inputTweetId for joins across engagement exports
Inspect per-row errors in the Errors output view instead of failing an entire batch
Facebook-heavy runs failing in a pattern? Use Issues on this Thunderbolt listing and attach your run ID plus one post, page, or search query
X Tweet Data Extractor collects publicly available data only. You are responsible for complying with platform terms, applicable laws, and data-protection regulations (including GDPR when personal data is involved). Consult your legal team for your specific use case.
$0.40 per 1,000 results — billed per successful row on Scrapy.io. You are charged only for successfully scraped rows. See Scrapy.io pricing for plan details.
Yes. Use webhooks, scheduled runs, or the publisher API to push data into your warehouse, CRM, or automation stack.
Yes. POST to https://thunderbolt.p.scrapy.io/x-tweet-scraper/v1/api with your API key. See the API tab on the Store listing for ready-made snippets.
Yes. Configure the Scrapy.io MCP endpoint with your API key to query X Tweet Data Extractor from AI assistants and agent workflows.
Thunderbolt focuses on Meta and social discovery workflows — leave a Reviews note if this actor saved you from browser scraping and which Facebook surface you use daily.
