100 free credits. No credit card required
X Tweet Replies API
Scrape X Tweet Replies data with one API call. Returns the replies to a specific tweet, each with the reply text, author info, like count, reply count, and creation timestamp. The tweet itself is not included in the list, and a tweet with no replies returns an empty list. Page size is set by the source. Deeper threads are a cursor walk: send `pagination.next_cursor` back as `cursor` and repeat until `pagination.has_more` is false; each page costs 1 credit. When the main source cannot fetch the first page, a second source answers with up to 40 direct replies and no cursor, so that response is the whole list.
Last updated October 2026Maintained by the SocialCrawl team
Returns the replies to a tweet, each with the reply text, author info, like and reply counts, and creation time. The tweet itself is not in the list.
Use it with a tweet URL to read the conversation under it; the cursor walks deeper threads, and a tweet with no replies returns an empty list.
Searching 68 platforms in parallel
·
TikTok·
Instagram·
YouTube·
Facebook·X·
LinkedIn·
Reddit·
Threads·
Pinterest·
Twitch·
Truth Social·
Snapchat·
Kick·
Bluesky·
Kwai·
Rumble·
Spotify·
Apple Music·TikTok Shop·
Amazon Shop·
Google Shopping·
Trustpilot·
TripAdvisor·
Yelp·
Linktree·
Komi·
Pillar·
lnk.bio·Facebook Ads·Google Ads·LinkedIn Ads·Google Search·Google News·
Finance·
Polymarket·
Tavily·
Hacker News·
GitHub·
Perplexity·
Naver·
Utility·
Universal Search
TikTok·Instagram·YouTube·Facebook·X·LinkedIn·Reddit·Threads·Pinterest·Twitch·Truth Social·Snapchat·Kick·Bluesky·Kwai·Rumble·Spotify·Apple Music·TikTok Shop·Amazon Shop·Google Shopping·Trustpilot·TripAdvisor·Yelp·Linktree·Komi·Pillar·lnk.bio·Facebook Ads·Google Ads·LinkedIn Ads·Google Search·Google News·Finance·Polymarket·Tavily·Hacker News·GitHub·Perplexity·Naver·Utility·Universal Search·TikTok·Instagram·YouTube·Facebook·X·LinkedIn·Reddit·Threads·Pinterest·Twitch·Truth Social·Snapchat·Kick·Bluesky·Kwai·Rumble·Spotify·Apple Music·TikTok Shop·Amazon Shop·Google Shopping·Trustpilot·TripAdvisor·Yelp·Linktree·Komi·Pillar·lnk.bio·Facebook Ads·Google Ads·LinkedIn Ads·Google Search·Google News·Finance·Polymarket·Tavily·Hacker News·GitHub·Perplexity·Naver·Utility·Universal Search
TikTok·Instagram·YouTube·Facebook·X·LinkedIn·Reddit·Threads·Pinterest·Twitch·Truth Social·Snapchat·Kick·Bluesky·Kwai·Rumble·Spotify·Apple Music·TikTok Shop·Amazon Shop·Google Shopping·Trustpilot·TripAdvisor·Yelp·Linktree·Komi·Pillar·lnk.bio·Facebook Ads·Google Ads·LinkedIn Ads·Google Search·Google News·Finance·Polymarket·Tavily·Hacker News·GitHub·Perplexity·Naver·Utility·Universal Search·TikTok·Instagram·YouTube·Facebook·X·LinkedIn·Reddit·Threads·Pinterest·Twitch·Truth Social·Snapchat·Kick·Bluesky·Kwai·Rumble·Spotify·Apple Music·TikTok Shop·Amazon Shop·Google Shopping·Trustpilot·TripAdvisor·Yelp·Linktree·Komi·Pillar·lnk.bio·Facebook Ads·Google Ads·LinkedIn Ads·Google Search·Google News·Finance·Polymarket·Tavily·Hacker News·GitHub·Perplexity·Naver·Utility·Universal Search
TikTok·Instagram·YouTube·Facebook·X·LinkedIn·Reddit·Threads·Pinterest·Twitch·Truth Social·Snapchat·Kick·Bluesky·Kwai·Rumble·Spotify·Apple Music·TikTok Shop·Amazon Shop·Google Shopping·Trustpilot·TripAdvisor·Yelp·Linktree·Komi·Pillar·lnk.bio·Facebook Ads·Google Ads·LinkedIn Ads·Google Search·Google News·Finance·Polymarket·Tavily·Hacker News·GitHub·Perplexity·Naver·Utility·Universal Search·TikTok·Instagram·YouTube·Facebook·X·LinkedIn·Reddit·Threads·Pinterest·Twitch·Truth Social·Snapchat·Kick·Bluesky·Kwai·Rumble·Spotify·Apple Music·TikTok Shop·Amazon Shop·Google Shopping·Trustpilot·TripAdvisor·Yelp·Linktree·Komi·Pillar·lnk.bio·Facebook Ads·Google Ads·LinkedIn Ads·Google Search·Google News·Finance·Polymarket·Tavily·Hacker News·GitHub·Perplexity·Naver·Utility·Universal Search
TikTok·Instagram·YouTube·Facebook·X·LinkedIn·Reddit·Threads·Pinterest·Twitch·Truth Social·Snapchat·Kick·Bluesky·Kwai·Rumble·Spotify·Apple Music·TikTok Shop·Amazon Shop·Google Shopping·Trustpilot·TripAdvisor·Yelp·Linktree·Komi·Pillar·lnk.bio·Facebook Ads·Google Ads·LinkedIn Ads·Google Search·Google News·Finance·Polymarket·Tavily·Hacker News·GitHub·Perplexity·Naver·Utility·Universal SearchX API
What can you do with the Tweet Replies API?
The Tweet Replies endpoint gives you structured X data with computed fields in a single request. No scraping infrastructure to build or maintain.
Example Request
curl -H "x-api-key: YOUR_API_KEY" \
"https://www.socialcrawl.dev/v1/twitter/tweet/replies?url=https%3A%2F%2Fx.com%2FNews24%2Fstatus%2F2093257170653483101&label=purchase_intent"import requests
response = requests.get(
"https://www.socialcrawl.dev/v1/twitter/tweet/replies",
params={
'url': 'https://x.com/News24/status/2093257170653483101',
'label': 'purchase_intent',
},
headers={"x-api-key": "YOUR_API_KEY"},
)
data = response.json()const response = await fetch(
"https://www.socialcrawl.dev/v1/twitter/tweet/replies?url=https%3A%2F%2Fx.com%2FNews24%2Fstatus%2F2093257170653483101&label=purchase_intent",
{
headers: { "x-api-key": "YOUR_API_KEY" },
},
);
const data = await response.json();Parameters worth sending
label=purchase_intentMarks each comment that shows buying intent, at no extra credits; sentiment, question and complaint are free too.
Parameters
| Parameter | Required | Description |
|---|---|---|
| url | Yes | Full URL of the tweet to fetch replies for |
| cursor | No | Cursor from the previous response's pagination.next_cursor to fetch more replies |
| 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`. |
Example Response
What does the X Tweet Replies API return?
Every response follows one unified schema. Here is a real, unmodified response body, so you can see the exact fields you get back before spending a credit.
Example response
{
"success": true,
"platform": "twitter",
"endpoint": "/v1/twitter/tweet/replies",
"data": {
"items": [
{
"comment": {
"id": "2093334825075716404",
"url": "https://example.com/redacted",
"parent_id": "2093257170653483101",
"post_id": "2093257170653483101",
"text": "Sample comment text (redacted).",
"author": {
"username": "user_e5f3d7",
"display_name": "user_d5bef4",
"avatar_url": "https://example.com/avatars/user_b94baf.png",
"verified": true
},
"engagement": {
"likes": 24,
"replies": 3
},
"flags": {
"pinned": null,
"deleted": false
},
"published_at": "2026-08-28T13:48:09.000Z",
"ext": {
"content_language": "en",
"author_followers": 1154,
"author_following": 706,
"author_posts_count": 43575,
"quote_count": 1,
"views": 3925,
"saves": 0
}
},
"computed": {
"language": "en",
"labels": {
"sentiment": {
"level": 2,
"score_0_1": 0.54,
"confidence": 0.17
},
"question": {
"p": 0.03
},
"purchase_intent": {
"p": 0.1
},
"complaint": {
"p": 0.05
}
}
}
},
{
"comment": {
"id": "2093280260154368190",
"url": "https://example.com/redacted",
"parent_id": "2093257170653483101",
"post_id": "2093257170653483101",
"text": "Sample comment text (redacted).",
"author": {
"username": "user_0843c0",
"display_name": "user_9c7700",
"avatar_url": "https://example.com/avatars/user_b2b11a.png",
"verified": true
},
"engagement": {
"likes": 6,
"replies": 0
},
"flags": {
"pinned": null,
"deleted": false
},
"published_at": "2026-08-28T10:11:19.000Z",
"ext": {
"content_language": "tl",
"author_followers": 6584,
"author_following": 7501,
"author_posts_count": 39317,
"quote_count": 0,
"views": 1380,
"saves": 0
}
},
"computed": {
"language": null,
"labels": {
"sentiment": {
"level": 1,
"score_0_1": 0.2,
"confidence": 0.68
},
"question": {
"p": 0.09
},
"purchase_intent": {
"p": 0.04
},
"complaint": {
"p": 0.09
}
}
}
}
],
"next_cursor": "DAAKCgABHTouZHi__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",
"total": null,
"labels": {
"presets": [
"sentiment",
"question"
],
"rows": 36,
"labelled": 36,
"cached": 36,
"skipped": 0,
"unjudged": 0,
"dropped": 0,
"dropped_ids": [],
"extra_credits": 0,
"methodology_version": "labels/1",
"mode": "default",
"status": "complete",
"pending": 0,
"pending_ids": []
},
"label_share": {
"policy_version": "share/1",
"presets": {
"question": {
"n": 36,
"counted": 1,
"abstained": 1,
"share": 0.0286,
"low": 0.0064,
"high": 0.1184,
"receipts": [
{
"id": "2093336263382155464",
"text": "Sample comment text (redacted)."
}
],
"policy_version": "share/1",
"too_few": false,
"at_cut": 1,
"nulls": 0,
"low_confidence": 1
},
"purchase_intent": {
"n": 36,
"counted": 1,
"abstained": 0,
"share": 0.0278,
"low": 0.0062,
"high": 0.1154,
"receipts": [
{
"id": "2093894790798590253",
"text": "Sample comment text (redacted)."
}
],
"policy_version": "share/1",
"too_few": false,
"at_cut": 1,
"nulls": 0,
"low_confidence": 0
},
"complaint": {
"n": 36,
"counted": 0,
"abstained": 1,
"share": 0,
"low": 0,
"high": 0.0718,
"receipts": [],
"policy_version": "share/1",
"too_few": false,
"at_cut": 1,
"nulls": 0,
"low_confidence": 1
},
"sentiment": {
"positive": {
"n": 36,
"counted": 0,
"abstained": 6,
"share": 0,
"low": 0,
"high": 0.0827,
"receipts": [],
"policy_version": "share/1",
"too_few": false,
"at_cut": 0,
"nulls": 0,
"low_confidence": 6
},
"neutral": {
"n": 36,
"counted": 2,
"abstained": 6,
"share": 0.0667,
"low": 0.0223,
"high": 0.1827,
"receipts": [
{
"id": "2093336263382155464",
"text": "Sample comment text (redacted)."
},
{
"id": "2093395650016670041",
"text": "Sample comment text (redacted)."
}
],
"policy_version": "share/1",
"too_few": false,
"at_cut": 2,
"nulls": 0,
"low_confidence": 6
},
"negative": {
"n": 36,
"counted": 28,
"abstained": 6,
"share": 0.9333,
"low": 0.8173,
"high": 0.9777,
"receipts": [
{
"id": "2093280260154368190",
"text": "Sample comment text (redacted)."
},
{
"id": "2093273537075147064",
"text": "Sample comment text (redacted)."
}
],
"policy_version": "share/1",
"too_few": false,
"at_cut": 28,
"nulls": 0,
"low_confidence": 6
}
}
}
},
"comment_language": {
"policy_version": "share/1",
"n": 36,
"abstained": 8,
"judged_by": {
"script": 0,
"choice": 28
},
"shares": {
"en": {
"n": 36,
"counted": 27,
"abstained": 8,
"share": 0.9643,
"low": 0.8548,
"high": 0.992,
"receipts": [
{
"id": "2093334825075716404",
"text": "Sample comment text (redacted)."
},
{
"id": "2093273537075147064",
"text": "Sample comment text (redacted)."
}
],
"policy_version": "share/1",
"too_few": false
},
"nl": {
"n": 36,
"counted": 1,
"abstained": 8,
"share": 0.0357,
"low": 0.008,
"high": 0.1452,
"receipts": [
{
"id": "2093274966372290829",
"text": "Sample comment text (redacted)."
}
],
"policy_version": "share/1",
"too_few": false
}
}
},
"comment_recency": {
"policy_version": "d09-1",
"n": 36,
"counted": 36,
"abstained": 0,
"share_last_24h": 0,
"share_later_half": null,
"too_few": false,
"stage": "unknown",
"order": "default",
"recent_share_is_floor": true,
"receipts": []
},
"dropped": 0
},
"credits_used": 1,
"request_id": "req_example000000",
"cached": false,
"pagination": {
"next_cursor": "sc.eyJ2IjoyLCJjIjoiREFBS0NnQUJIVG91WkhpX19wVUxBQUlBQUFHb1JXMVFRelozUVVGQlpsRXZaMGRLVGpCMlIzQXZRVUZCUVVOVlpFUk5OVGwzT1dGU1JGSXdUVGxXSzBReGFraFlTRkY2Vmk5RE4xZFJUVWxrUkU1d1ltbEtXWGg0VWpCT1FrODJNR3czUlRCSVVUQTRVVW95V0dOV2EyUkVWVll6THpGWlVXdDRNRTB3VFVJeE1XbElVVWhSZWxoc1JGTllRV1JKWkVSUldUbHNiRnBCZVVJd1RpOXVSRXhXYlVoeFNGRXpURkp3VEZkalJIZGtSRkp5VEhsd1duZG1lREJPUVV4dFdWWnBRbWhJVVhwamJEZGFXRUZXV1dSRVRtMUdjVVprWjFwU01FNUdNVm93YkdwREswaFJlbVJ0TmtoWE9FVnJaRVJSV1daYWVHSjRUa0l3VFRBd05WSXhjVU1yU0ZGNlpEQmtiMWd3U1VWa1JFNWpNM2hzWmxGS2FEQk5lbFJFTjBaMlJUUklVWHBpYWpWU1YwMUlNR1JFVFZkS2VGcGpRV3RTTUUweWIyMWliREJDYUVoUmVtbHpkWGRZYzFGTlpFUlZPVlJtTlZsblIzZ3dUVFJPTDBGV2IwWmhTRkY2YjBsaGFGZG5SemhrUkV3MVQxbEdZMUZZVWpCTmVrWlNaMjFvUkd0SVVYcFRUbU5vVjJkRFdXUkVWVEJ0Y1hSa1FuZENNRTB6WkRnNWJERkZiRWhSZWpKSVVHdFhWVUpaWkVSM1NUTTBjR1JDVEZFOVBRZ0FBd0FBQUFJTEFBUUFBQUFHUW05MGRHOXRBQUEiLCJlIjoidHdpdHRlci90d2VldC9yZXBsaWVzIiwicCI6ImN1cnNvciJ9",
"has_more": true,
"page_size": 36
},
"credits_remaining": 9999,
"source": "captured",
"captured_at": "2026-10-02T15:40:56.239Z",
"redacted": true
}Live sample illustrating the unified response shape. Field values reflect the record you query.
API Details
How does the X Tweet Replies API work?
Send a GET request with your API key and get back clean, structured JSON in our unified schema. Supported computed fields are populated when the source provides the required inputs.
Method
GET
Response
JSON
Developer First
How do you scrape social media data in seconds?
The fastest social media scraping API for developers. Scrape profiles, posts, comments, and analytics from 68 platforms covering 10B+ monthly active users.
One schema, every platform
Query 68 platforms with identical response structures. Write your integration once.
Computed fields, not just scraped
When an endpoint supports these metrics and the source provides the required inputs, the normalized record includes engagement_rate, estimated_reach, content_category, and language. Ready to use.
See your data before you code
Visual Data Explorer. Paste any URL, get rich result cards, sortable tables, CSV export.
import requests
response = requests.get(
'https://www.socialcrawl.dev/v1/tiktok/profile',
params={'handle': 'charlidamelio'},
headers={'x-api-key': 'sc_YOUR_API_KEY'}
)
data = response.json()[ .JSON ]
{
"success": true,
"platform": "tiktok",
"data": {
"author": {
"username": "charlidamelio",
"followers": 152400000
},
"engagement": {
"likes": 12400000000,
"engagement_rate": 0.087
},
"metadata": {
"language": "en",
"content_category": "lifestyle"
}
}
}Ready to scrape X Tweet Replies data?
Get your API key and start pulling X data in under 60 seconds.