Instagram Search Mentions API
Scrape Instagram Search Mentions data with one API call. Returns Instagram posts by other accounts that tag an account or @mention it in the caption. Each call runs two searches: the account's Tagged tab (/v1/instagram/tagged, `match_kind` `tag`) and reels whose caption mentions `@handle` (/v1/instagram/search/reels, kept only when the caption carries the @handle or links the profile, `match_kind` `mention` or `link`). The Tagged tab is the one Instagram surface for posts that tag an account, and an account that approves its tags by hand can show older posts there, which is why the caption search runs as well. The account's own posts are left out unless `include_self=true`. Rows are merged newest first and a post found twice appears once. Each row in `data.items` is exactly the row the named search endpoint returns, with its `post` and `computed` blocks, plus `match_kind`. `since` and `until` keep only rows published inside the window. Send `data.next_cursor` back as `cursor` for the next page; it is null when every search has run out. `data.legs` reports each search: its endpoint, status, rows returned and kept, credits and `request_id`. `data.excluded` counts the rows left out (`unmatched`, `self`, `duplicate`, `outside_window`). Each call is one page of each search at its own price: 5 credits for the Tagged page and 1 for the reels page, so at most 6.
Last updated October 2026Maintained by the SocialCrawl team
Returns Instagram posts by other accounts that tag an account or @mention it in a reel caption, merged newest first, each labelled tag, mention or link.
Use it to track who tags or mentions a brand on Instagram, including brands whose Tagged tab is curated; since/until keep rows inside a date window.
Searching 68 platforms in parallel
What can you do with the Search Mentions API?
The Search Mentions endpoint gives you structured Instagram 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/instagram/search/mentions?handle=myntra"import requests
response = requests.get(
"https://www.socialcrawl.dev/v1/instagram/search/mentions",
params={
'handle': 'myntra',
},
headers={"x-api-key": "YOUR_API_KEY"},
)
data = response.json()const response = await fetch(
"https://www.socialcrawl.dev/v1/instagram/search/mentions?handle=myntra",
{
headers: { "x-api-key": "YOUR_API_KEY" },
},
);
const data = await response.json();Parameters
| Parameter | Required | Description |
|---|---|---|
| handle | Yes | The account's username, with or without the @, or its profile URL. |
| cursor | No | data.next_cursor from the previous response, sent back verbatim for the next page. |
| since | No | Only posts published on or after this date, YYYY-MM-DD (UTC). |
| until | No | Only posts published on or before this date, YYYY-MM-DD (UTC); that whole day is included. |
| include_self | No | When true, also keep posts by the account itself. Default false. |
What does the Instagram Search Mentions 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": "instagram",
"endpoint": "/v1/instagram/engagement",
"data": {
"engagement_rate_percentages": 38.33,
"recent_posts": 12,
"followers": 87608035,
"comments": 528912,
"likes": 33049046,
"recent_posts_explanation": "Statistics based on the last 12 posts",
"id_user": "2278169415",
"username": "mrbeast",
"is_private": false,
"posts_details": [
{
"likes": 5636982,
"comments": 69484,
"taken_at": 1781457954,
"datetime": "2026-06-14 20:25:54",
"hours_since_post": 461,
"time_ago": "19 days ago",
"likes_per_hour": 12228,
"comments_per_hour": 151
},
{
"likes": 20000768,
"comments": 223510,
"taken_at": 1732824650,
"datetime": "2024-11-28 23:10:50",
"hours_since_post": 13971,
"time_ago": "2 years ago",
"likes_per_hour": 1432,
"comments_per_hour": 16
},
{
"likes": 929226,
"comments": 30633,
"taken_at": 1782232475,
"datetime": "2026-06-23 19:34:35",
"hours_since_post": 246,
"time_ago": "10 days ago",
"likes_per_hour": 3777,
"comments_per_hour": 125
},
{
"likes": 487761,
"comments": 22482,
"taken_at": 1781799425,
"datetime": "2026-06-18 19:17:05",
"hours_since_post": 366,
"time_ago": "15 days ago",
"likes_per_hour": 1333,
"comments_per_hour": 61
},
{
"likes": 712265,
"comments": 15716,
"taken_at": 1781366405,
"datetime": "2026-06-13 19:00:05",
"hours_since_post": 487,
"time_ago": "20 days ago",
"likes_per_hour": 1463,
"comments_per_hour": 32
},
{
"likes": 1475116,
"comments": 35386,
"taken_at": 1781277094,
"datetime": "2026-06-12 18:11:34",
"hours_since_post": 512,
"time_ago": "21 days ago",
"likes_per_hour": 2881,
"comments_per_hour": 69
},
{
"likes": 1108220,
"comments": 26632,
"taken_at": 1780160249,
"datetime": "2026-05-30 19:57:29",
"hours_since_post": 822,
"time_ago": "1 months ago",
"likes_per_hour": 1348,
"comments_per_hour": 32
},
{
"likes": 542948,
"comments": 28476,
"taken_at": 1779375582,
"datetime": "2026-05-21 17:59:42",
"hours_since_post": 1040,
"time_ago": "1 months ago",
"likes_per_hour": 522,
"comments_per_hour": 27
},
{
"likes": 698514,
"comments": 24401,
"taken_at": 1779120014,
"datetime": "2026-05-18 19:00:14",
"hours_since_post": 1111,
"time_ago": "2 months ago",
"likes_per_hour": 629,
"comments_per_hour": 22
},
{
"likes": 468000,
"comments": 13548,
"taken_at": 1778947209,
"datetime": "2026-05-16 19:00:09",
"hours_since_post": 1159,
"time_ago": "2 months ago",
"likes_per_hour": 404,
"comments_per_hour": 12
},
{
"likes": 526594,
"comments": 24411,
"taken_at": 1777737719,
"datetime": "2026-05-02 19:01:59",
"hours_since_post": 1495,
"time_ago": "2 months ago",
"likes_per_hour": 352,
"comments_per_hour": 16
},
{
"likes": 462652,
"comments": 14233,
"taken_at": 1777580305,
"datetime": "2026-04-30 23:18:25",
"hours_since_post": 1538,
"time_ago": "2 months ago",
"likes_per_hour": 301,
"comments_per_hour": 9
}
]
},
"credits_used": 5,
"credits_remaining": 9999,
"request_id": "req-8Kq2ZmR4vT9xLb3P",
"cached": false,
"redacted": true
}Live sample illustrating the unified response shape. Field values reflect the record you query.
How does the Instagram Search Mentions 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
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(){
"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 Instagram Search Mentions data?
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