$6/1000
results
Fetch detailed Facebook comment records by comment ID. Returns author, text, engagement counters, and related metadata for each requested comment. Handy when you already have comment IDs from another scrape and need the full comment payload.
Fetch detailed Facebook comment records by comment ID. Returns author, text, engagement counters, and related metadata for each requested comment. Handy when you already have comment IDs from another scrape and need the full comment payload.
Hydrate comment IDs into full comment records after discovery via post comment scrapers.
On Scrapy.io, Facebook Comment Scraper by Data Doping is built for facebook comment scraper workflows — run from the Store, schedule recurring jobs, or POST to datadoping.p.scrapy.io from your stack.
commentIds or inputFacebook and Meta properties still 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 Scraper uses pay-per-result billing with clear input caps — ideal when you are sweeping thousands of pages or keywords on a schedule rather than clicking through the UI manually.
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 Scraper 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 Scraper:
maxResults, maxReviews, or equivalent limits so a full batch stays within budgetdatadoping.p.scrapy.io/facebook-comment-scraper/v1/api or create a scheduled jobMost teams keep a "golden" input file in git for Facebook Comment Scraper 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.
re-fetch comments flagged by classifiers
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 Data Doping 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.
archive specific comments cited in reports
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 Data Doping 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.
enrich IDs from bulk comment exports
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 Data Doping 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 comment text with timestamps
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 Data Doping 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.
Data Doping Facebook tools on Scrapy.io cover pages, profiles, groups, Marketplace, and the Ad Library — the surfaces brands monitor for creative intelligence, community management, and local commerce. Executor-accurate inputs, maxResults caps, and pay-per-result billing let data teams run repeatable jobs without maintaining Meta scrapers in-house.
Facebook Comment Scraper is rarely the only step in a data workflow. Teams typically:
See Related Data Doping 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 Scraper 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 Data Doping tools on those stable keys to build multi-step datasets without re-scraping upstream steps.
Maintaining Instagram, LinkedIn, X, TikTok, or Facebook extractors in-house means handling rate limits, HTML or API changes, proxy rotation, and observability — work that rarely differentiates your product. Facebook Comment Scraper on Scrapy.io outsources that operational burden: you supply inputs, Scrapy.io runs the job, and you pay per successful row.
Data Doping listings add warehouse-first 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 Scraper 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 Scraper once from the Store to validate inputs, then promote the same JSON payload to a scheduled job on Scrapy.io. Common patterns:
datadoping.p.scrapy.io on a queueData Doping tools use pay-per-result billing, so scheduled jobs stay predictable when you cap max_count, max_posts, max_comments, or equivalent limits in the input form.
curl -X POST "https://datadoping.p.scrapy.io/facebook-comment-scraper/v1/api" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"commentIds": ["1234567890123456"]
}'Bulk runs via the scraper endpoint:
curl -X POST "https://datadoping.p.scrapy.io/facebook-comment-scraper/v1/scraper" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"commentIds": ["1234567890123456"]
}'Open the API tab on the Store listing for Python, JavaScript, cURL, OpenAPI, and MCP snippets.
Facebook Comment Scraper 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 Scraper for MCP connection snippets.
| Tool | Best for |
|---|---|
| Facebook Page Scraper | Data Doping companion tool — overview |
| Facebook Profile Scraper | Data Doping companion tool — overview |
| Facebook Page Posts Scraper | Data Doping 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
If the same social scrape keeps failing, open Issues on this Data Doping listing — include your run ID and one sample URL or handle
Facebook Comment Scraper 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.
Yes. Use webhooks, scheduled runs, or the publisher API to push data into your warehouse, CRM, or automation stack.
Yes. POST to https://datadoping.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 Scraper from AI assistants and agent workflows.
Data Doping ships frequent Instagram, LinkedIn, TikTok, and Facebook updates — if this scrape helps your stack, leave a Reviews rating and tell us which network or field to improve next.
Hydrate comment IDs into full comment records after discovery via post comment scrapers.
On Scrapy.io, Facebook Comment Scraper by Data Doping is built for facebook comment scraper workflows — run from the Store, schedule recurring jobs, or POST to datadoping.p.scrapy.io from your stack.
commentIds or inputFacebook and Meta properties still 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 Scraper uses pay-per-result billing with clear input caps — ideal when you are sweeping thousands of pages or keywords on a schedule rather than clicking through the UI manually.
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 Scraper 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 Scraper:
maxResults, maxReviews, or equivalent limits so a full batch stays within budgetdatadoping.p.scrapy.io/facebook-comment-scraper/v1/api or create a scheduled jobMost teams keep a "golden" input file in git for Facebook Comment Scraper 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.
re-fetch comments flagged by classifiers
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 Data Doping 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.
archive specific comments cited in reports
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 Data Doping 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.
enrich IDs from bulk comment exports
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 Data Doping 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 comment text with timestamps
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 Data Doping 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.
Data Doping Facebook tools on Scrapy.io cover pages, profiles, groups, Marketplace, and the Ad Library — the surfaces brands monitor for creative intelligence, community management, and local commerce. Executor-accurate inputs, maxResults caps, and pay-per-result billing let data teams run repeatable jobs without maintaining Meta scrapers in-house.
Facebook Comment Scraper is rarely the only step in a data workflow. Teams typically:
See Related Data Doping 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 Scraper 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 Data Doping tools on those stable keys to build multi-step datasets without re-scraping upstream steps.
Maintaining Instagram, LinkedIn, X, TikTok, or Facebook extractors in-house means handling rate limits, HTML or API changes, proxy rotation, and observability — work that rarely differentiates your product. Facebook Comment Scraper on Scrapy.io outsources that operational burden: you supply inputs, Scrapy.io runs the job, and you pay per successful row.
Data Doping listings add warehouse-first 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 Scraper 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 Scraper once from the Store to validate inputs, then promote the same JSON payload to a scheduled job on Scrapy.io. Common patterns:
datadoping.p.scrapy.io on a queueData Doping tools use pay-per-result billing, so scheduled jobs stay predictable when you cap max_count, max_posts, max_comments, or equivalent limits in the input form.
curl -X POST "https://datadoping.p.scrapy.io/facebook-comment-scraper/v1/api" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"commentIds": ["1234567890123456"]
}'Bulk runs via the scraper endpoint:
curl -X POST "https://datadoping.p.scrapy.io/facebook-comment-scraper/v1/scraper" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"commentIds": ["1234567890123456"]
}'Open the API tab on the Store listing for Python, JavaScript, cURL, OpenAPI, and MCP snippets.
Facebook Comment Scraper 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 Scraper for MCP connection snippets.
| Tool | Best for |
|---|---|
| Facebook Page Scraper | Data Doping companion tool — overview |
| Facebook Profile Scraper | Data Doping companion tool — overview |
| Facebook Page Posts Scraper | Data Doping 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
If the same social scrape keeps failing, open Issues on this Data Doping listing — include your run ID and one sample URL or handle
Facebook Comment Scraper 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.
Yes. Use webhooks, scheduled runs, or the publisher API to push data into your warehouse, CRM, or automation stack.
Yes. POST to https://datadoping.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 Scraper from AI assistants and agent workflows.
Data Doping ships frequent Instagram, LinkedIn, TikTok, and Facebook updates — if this scrape helps your stack, leave a Reviews rating and tell us which network or field to improve next.
