$6.45/1000
results
Hydrate Facebook comment IDs into full comment payloads — author, text, and engagement counters for enrichment queues. $6.45 per 1,000 comments.
Hydrate Facebook comment IDs into full comment payloads — author, text, and engagement counters for enrichment queues. $6.45 per 1,000 comments.
Start from known comment IDs — return full public comment payloads in batch for moderation and NLP pipelines.
On Scrapy.io, Facebook Comment Data Extractor by Thunderbolt is built for facebook comment scraper workflows — run from the Store, schedule recurring jobs, or POST to thunderbolt.p.scrapy.io from your stack.
commentIds or inputFacebook and Meta properties anchor social advertising, local business discovery, and community groups — pages, profiles, Marketplace listings, Ad Library creatives, and comment threads across a huge public graph. A facebook comment scraper pipeline on Scrapy.io turns URLs and search queries into structured rows for lead-gen and research.
Facebook Comment Data Extractor uses pay-per-result billing with clear input caps — ideal when you are sweeping thousands of pages or keywords on a schedule. Thunderbolt Facebook tools chain as discovery hops (search → resolve → deep scrape) so each step bills only for successful rows.
This facebook comment scraper 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 Facebook Comment Data Extractor while researching facebook comment scraper and related terms such as . 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 Facebook Comment Data Extractor:
maxResults, maxReviews, or equivalent limits so a full batch stays within budgetthunderbolt.p.scrapy.io/facebook-comment-scraper/v1/api or create a scheduled jobMost teams keep a "golden" input file in git for Facebook Comment 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.
Fetch individual comments by numeric comment ID.
Configure the run in the Scrapy.io input form or pass JSON to the publisher API.
{
"commentIds": [
"1234567890123456"
]
}| Field | Type | Required | Description |
|---|---|---|---|
commentIds | string[] | Yes | Facebook comment IDs. One per line. Also accepted: commentId (Single ID alias); or input (Single ID alias). |
Results appear in the Scrapy.io run Output tab. Comments and Errors tables in the run output.
Each successful row is designed for warehouse loads: stable column names, explicit error rows, and caps you control in the input form.
{
"commentIds": ["1234567890123456"]
}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.
expand thin comment refs into full rows
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.
inspect flagged comment IDs with full context
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.
attach body text before NLP
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.
resolve comment IDs from tickets
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.
ID-in / payload-out design aimed at API callers, not browsers. Prefer Thunderbolt when you already have comment IDs and need reliable hydration without re-scraping an entire post thread.
Facebook Comment 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 mattersFacebook Comment 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. Facebook Comment 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.
Facebook Comment 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 Facebook Comment 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/facebook-comment-scraper/v1/api" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"commentIds": ["1234567890"]
}'Bulk runs via the scraper endpoint:
curl -X POST "https://thunderbolt.p.scrapy.io/facebook-comment-scraper/v1/scraper" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"commentIds": ["1234567890"]
}'Open the API tab on the Store listing for Python, JavaScript, cURL, OpenAPI, and MCP snippets.
Facebook Comment 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 Facebook Comment Data Extractor for MCP connection snippets.
| Tool | Best for |
|---|---|
| Facebook Page Data Extractor | Thunderbolt companion tool — overview |
| Facebook Profile Insights Scraper | Thunderbolt companion tool — overview |
| Facebook Page Posts Collector | Thunderbolt companion tool — overview |
Start with maxResults: 50 on test runs, then scale after validating schema
Batch many targets per job — failed rows appear in the Errors view
Store scrapedAt timestamps for time-series dashboards
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
Facebook Comment 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.
$6.00 per 1,000 comments — 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/facebook-comment-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 Facebook Comment 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 known comment IDs — return full public comment payloads in batch for moderation and NLP pipelines.
On Scrapy.io, Facebook Comment Data Extractor by Thunderbolt is built for facebook comment scraper workflows — run from the Store, schedule recurring jobs, or POST to thunderbolt.p.scrapy.io from your stack.
commentIds or inputFacebook and Meta properties anchor social advertising, local business discovery, and community groups — pages, profiles, Marketplace listings, Ad Library creatives, and comment threads across a huge public graph. A facebook comment scraper pipeline on Scrapy.io turns URLs and search queries into structured rows for lead-gen and research.
Facebook Comment Data Extractor uses pay-per-result billing with clear input caps — ideal when you are sweeping thousands of pages or keywords on a schedule. Thunderbolt Facebook tools chain as discovery hops (search → resolve → deep scrape) so each step bills only for successful rows.
This facebook comment scraper 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 Facebook Comment Data Extractor while researching facebook comment scraper and related terms such as . 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 Facebook Comment Data Extractor:
maxResults, maxReviews, or equivalent limits so a full batch stays within budgetthunderbolt.p.scrapy.io/facebook-comment-scraper/v1/api or create a scheduled jobMost teams keep a "golden" input file in git for Facebook Comment 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.
Fetch individual comments by numeric comment ID.
Configure the run in the Scrapy.io input form or pass JSON to the publisher API.
{
"commentIds": [
"1234567890123456"
]
}| Field | Type | Required | Description |
|---|---|---|---|
commentIds | string[] | Yes | Facebook comment IDs. One per line. Also accepted: commentId (Single ID alias); or input (Single ID alias). |
Results appear in the Scrapy.io run Output tab. Comments and Errors tables in the run output.
Each successful row is designed for warehouse loads: stable column names, explicit error rows, and caps you control in the input form.
{
"commentIds": ["1234567890123456"]
}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.
expand thin comment refs into full rows
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.
inspect flagged comment IDs with full context
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.
attach body text before NLP
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.
resolve comment IDs from tickets
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.
ID-in / payload-out design aimed at API callers, not browsers. Prefer Thunderbolt when you already have comment IDs and need reliable hydration without re-scraping an entire post thread.
Facebook Comment 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 mattersFacebook Comment 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. Facebook Comment 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.
Facebook Comment 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 Facebook Comment 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/facebook-comment-scraper/v1/api" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"commentIds": ["1234567890"]
}'Bulk runs via the scraper endpoint:
curl -X POST "https://thunderbolt.p.scrapy.io/facebook-comment-scraper/v1/scraper" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"commentIds": ["1234567890"]
}'Open the API tab on the Store listing for Python, JavaScript, cURL, OpenAPI, and MCP snippets.
Facebook Comment 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 Facebook Comment Data Extractor for MCP connection snippets.
| Tool | Best for |
|---|---|
| Facebook Page Data Extractor | Thunderbolt companion tool — overview |
| Facebook Profile Insights Scraper | Thunderbolt companion tool — overview |
| Facebook Page Posts Collector | Thunderbolt companion tool — overview |
Start with maxResults: 50 on test runs, then scale after validating schema
Batch many targets per job — failed rows appear in the Errors view
Store scrapedAt timestamps for time-series dashboards
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
Facebook Comment 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.
$6.00 per 1,000 comments — 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/facebook-comment-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 Facebook Comment 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.
