# Facebook Post Comment Replies API (https://www.socialcrawl.dev/platforms/facebook/post-comment-replies)

> Returns the reply thread for a single Facebook comment. Both `feedback_id` and `expansion_token` come from the parent `/v1/facebook/post/comments` response: note that `feedback_id` is NOT the comment ID. Forward the returned cursor for additional pages. Each reply carries `comment.parent_id`, the `comment.id` of the comment it answers. An `expansion_token` belongs to its comment and must be passed whole (a complete token is about 750 characters). Tokens two and a half days old still return their replies; an expired or incomplete token returns an empty list, the same answer as a comment whose replies are all hidden, so every empty page carries a `stale_expansion_token` note in `_warnings` with the token's issue time and length. Read the comment again from `/v1/facebook/post/comments` for a fresh token.

TL;DR: `GET /v1/facebook/post/comment/replies` costs 1 credit per page, with the default labels (sentiment, question, purchase_intent, complaint) included free. label=spam, toxic or low_quality holds 4 extra credits and refunds down to 1 credit per started 25 comments that were newly judged: comments already labelled are free, a page where nothing could be judged refunds the whole extra, and a cached page is free. With `max_pages`, `max_items` or `max_credits`, each page walked is billed at this price (a cached page is free), and `max_credits` caps the total. and returns SocialCrawl's unified JSON schema. Single x-api-key auth, 100 free credits on signup.

## Parameters

| Parameter | Required | Description |
| --- | --- | --- |
| `feedback_id` | yes | `feedback_id` from `/v1/facebook/post/comments` for the parent comment (this is NOT the comment ID). |
| `expansion_token` | yes | `expansion_token` from `/v1/facebook/post/comments` for the parent comment. |
| `cursor` | no | Cursor returned by the previous response for pagination. |
| `label` | no | Optional CSV of SocialCrawl labels to add to every comment: sentiment, spam, question, purchase_intent, complaint, toxic, low_quality, injection. Without this param every page already carries sentiment, question, purchase_intent and complaint, free; label= adds the labels you name to them (the defaults keep running). Each comment gains computed.labels with probabilities (0 to 1), so you choose the cut-off; a comment that could not be judged carries labels: null. sentiment, question, purchase_intent and complaint are free when asked for too; spam, toxic, low_quality and injection add 1 credit per started 25 newly judged comments (comments already labelled for anyone are free, and so is a cached page). injection flags text that addresses an AI system and tries to direct it (flagged, p); it never drops or rewrites a row. judgments=off (or label=none) turns the default labels off. data.labels reports what was judged and billed. |
| `exclude` | no | Optional CSV (spam, low_quality), only with the same preset in label. Drops comments whose probability is 0.8 or higher and lists their ids in data.labels.dropped_ids. A comment that could not be judged is never dropped. |
| `judgments` | no | Optional, on (the default) or off. By default every row gains free SocialCrawl judgments (computed.labels, and computed.relevance on search endpoints), reported in data.labels (mode default) and data.relevance (origin default), each with a status (complete, partial or skipped) and pending: the rows still being judged when the page was sent, which carry null now and are filled on your next call or cached read. Default judgments never add credits, never change an existing field, and never drop or reorder a row. off returns the page exactly as before, with none of those keys. label=none does the same. (on \| off) |
| `dry_run` | no | Optional. When 1, return a cost preview for this labelled or relevance-filtered request without fetching the page or judging any row. data.estimate reports rows_expected, rows_cached, label_credits_min, label_credits_max and base_credits. 0 credits charged. (1) |
| `label_evidence` | no | Optional, only with label=. When 1, every labelled row also carries computed.labels_evidence.<preset> = { quote, sentence_index }: the sentence in the row that most clearly shows the label, copied verbatim. Absent or null when no single sentence shows it. (1) |
| `fit` | no | Optional. When goal, keep the rows and fields needed for the goal you pass in goal= (plus any that are uncertain, and the first and last), and replace the rest with a stub. data.held_back lists the held ids and a recall id that re-reads the full page from cache at no extra charge. Without this param the page is unchanged. (goal) |
| `goal` | no | Required by fit=goal. What you are trying to do, in your own words, up to 300 characters. |
| `fit_tokens` | no | Optional, only with fit=goal. Soft cap on how much of the page to keep, in tokens. Uncertain blocks and the first and last block are kept even if they exceed it. |
| `since` | no | Only rows published on or after this date: YYYY-MM-DD (midnight UTC) or an ISO 8601 timestamp. Rows older than it are left off each page. This list is not in date order, so the walk does not stop at the date: keep following `next_cursor`, and each page costs what a page costs even when every row on it is filtered out. A row with no date is kept. `published_after`, `start_date`, `from_date`, `publish_time` (`yesterday`, `this-week`, `this-month`, `last-3-months`, `last-6-months`) and `max_age_days` are accepted and applied as `since`. |
| `until` | no | Only rows published on or before this date: YYYY-MM-DD (that whole UTC day is included) or an ISO 8601 timestamp. Newer rows are left off each page and the walk does not stop, so older pages follow. A row with no date is kept. `published_before`, `end_date` and `to_date` are accepted and applied as `until`. |
| `max_pages` | no | 1 to 50 (default 1, or 50 when `max_items` or `max_credits` is sent). Walk up to this many pages in one call and return the rows from all of them. Each page walked is billed exactly as one call to this endpoint (a cached page is free) and counts against your rate limit; its charge and any refund are recorded under the page's own request id, listed in `data.walk.request_ids`. The walk stops early at the last page, or when the request's time budget runs low; `data.walk.stopped` says why (`end`, `max_pages`, `max_items`, `max_credits`, `time_budget`, `page_error`) and `pagination.next_cursor` continues from where it stopped. |
| `max_items` | no | 1 to 5000. Get more in one call: keep walking pages until the response holds at least this many rows, then stop with `data.walk.stopped: "max_items"`. The last page is returned whole (it was billed), so `data.items` can hold up to one page more than this, and `pagination.next_cursor` resumes right after it without skipping a row. Walks up to 50 pages unless `max_pages` says fewer; each page is billed as one call (a cached page is free). Add `max_credits` to cap the spend. A long walk can stop on the time budget first (`"time_budget"`): send the returned cursor with the same params to continue. |
| `max_credits` | no | 1 to 100000. The most this call may spend, across every page it walks. A page starts only while the credits already spent plus that page's price (or, when higher, the most any page of this walk has cost) still fit, so the call never goes past it; the walk then stops with `data.walk.stopped: "max_credits"` and the cursor to resume. Sent alone, it walks as far as the budget goes (up to 50 pages). A value below the price of one page is a free 400 whose `error.details.page_credits` names that price. Credits a row join (`include=`) adds are known only once a page has run, so page 1 is checked against its own price. |

## Code example

```bash
curl -G "https://www.socialcrawl.dev/v1/facebook/post/comment/replies" \
  --data-urlencode "feedback_id=ZmVlZGJhY2s6MzM3MzYxMzI5OTQ4Nzc1NV8xOTI5NTk0OTE3Njg3MTcz" \
  --data-urlencode "expansion_token=MjoxNzc4NjM1MDM3OgF_JaMa3kMP9RIWC" \
  -H "x-api-key: sc_YOUR_API_KEY"
```

## FAQ

### How do I get the replies to a Facebook comment?

Send a GET request to /v1/facebook/post/comment/replies with a feedback_id and an expansion_token. SocialCrawl returns the nested replies under that specific top-level comment, each with author, text, like count, and timestamp.

### Where do feedback_id and expansion_token come from?

Both come from the /v1/facebook/post/comments response. Each top-level comment there carries its own feedback_id and expansion_token. Take those two values for the comment you want to expand and pass them to this endpoint to pull its reply layer.

### How much does the comment replies endpoint cost?

Each request costs 1 credit on the standard tier. New accounts start with 100 free credits and no credit card, so you can expand 100 reply threads before paying. The response is a single synchronous JSON payload.

### How do I paginate through a long reply thread?

Pass the optional cursor parameter to walk deeper into a comment with many replies. Each response returns the next cursor; keep calling until it comes back empty. SocialCrawl handles the underlying pagination tokens for you.

### Why call this instead of the top-level comments endpoint?

/v1/facebook/post/comments returns top-level comments with a reply count but does not expand the nested layer. This endpoint fills that gap, letting you reconstruct full comment threads for community-sentiment pipelines and conversation analysis.

See the full Facebook API: https://www.socialcrawl.dev/platforms/facebook

## Pricing

- Standard endpoints: 1 credit per call
- Advanced endpoints: 5 credits per call
- Premium endpoints: 10 credits per call
- 100 free credits on signup, no credit card required. Cached responses cost 0 credits. Credit packs never expire.
- Full pricing: https://www.socialcrawl.dev/pricing

## Explore with AI

Questions this API answers, phrased for an AI assistant:

- How do I fetch the nested replies under a specific Facebook comment via API?
- How does SocialCrawl use feedback_id and expansion_token to expand Facebook comment threads?
- How can I reconstruct a full Facebook comment thread for sentiment analysis?