Instagram Tagged & Mentions Scraper: Scrape Comments & Replies

Instagram Scraper — Extract Tagged Posts, Mentions & Comments as JSON

Instagram Tagged & Mentions Posts Scraper pulls every public post where an Instagram account is tagged, straight from Instagram's own Tagged-tab feed, and flags whether the caption also @mentions that account. Each result returns typed, normalized JSON — captions, engagement counts, tagged-user lists, top comments, and sponsorship flags, with no HTML and no selectors to parse. Point it at one or more usernames and get back structured data ready for a spreadsheet, a database, or an LLM context window. No Instagram login is required to run it.

What is Instagram Tagged & Mentions Posts Scraper?

Instagram Tagged & Mentions Posts Scraper is an Apify Actor that reads a target account's real Tagged-tab feed (PolarisProfileTaggedTabContentQuery — the same GraphQL query instagram.com/username/tagged/ uses) and returns every post in it, with each caption also checked for an @mentioned_user match. It runs fully logged-out, so no Instagram account or session cookie is needed. Results export as JSON, CSV, or Excel from the Apify dataset.

• Scrape posts from an account's real Tagged-tab feed
• Detect caption @mentions of the same target account on those posts
• Pull up to 50 top comments per post
• Capture paid-partnership, affiliate, and ad disclosure flags
• Export as JSON, CSV, or Excel — no proxy setup required

What data does Instagram Tagged & Mentions Posts Scraper collect?

Every result bundles four kinds of data in one JSON record: the post itself, the poster's profile snapshot, the accounts tagged in it, and its top comments.

Data Type	Key Fields	JSON Field Names
Tagged & mentioned posts	caption, engagement, publish date, media	caption, like_count, comment_count, video_view_count, post_date, display_url, video_url
Sponsorship signals	paid partnership, affiliate, ad, sponsor data	is_paid_partnership, is_affiliate, is_ad, sponsor_user
Tagged & mentioned profile data	poster identity, tagged accounts, mention match	owner.username, owner.full_name, owner.is_verified, tagged_users[].username, mentioned_user
Comments	comment text, author, likes	latest_comments[].text, latest_comments[].owner.username, latest_comments[].edge_liked_by.count

Need more Instagram data?

If you need inbound-mention tracking with keyword filters and cross-run new-mention detection instead of the Tagged tab specifically, Instagram Mentions Scraper With Brand Mention Tracker is built for that. If you need to filter tagged posts down to only the ones where a second, specific account is also tagged alongside your target, Instagram Tagged Posts & Co-Tagged Profiles Scraper adds that co-tag filter on top of the same Tagged-tab feed.

How does Instagram Tagged & Mentions Posts Scraper differ from the official Instagram API?

Instagram's official Graph API and Instagram Basic Display API do not expose an arbitrary "who tagged or mentioned this account" lookup for third-party public accounts — they are scoped to data belonging to the Business or Creator account that has connected its own app, after Meta's App Review process approves the requested permissions.

Feature	Instagram Graph API	Instagram Tagged & Mentions Posts Scraper
Accessible accounts	Only the app's own connected Business/Creator account	Any public Instagram account
Tagged/mention lookup for third parties	Not exposed	Core function
Approval process	Meta App Review + Business Verification required	None — run immediately
Authentication	Facebook Login + long-lived access token	No login or session required
Output format	Versioned Graph API JSON, subject to API deprecation cycles	Stable Apify dataset schema (JSON/CSV/Excel)
Setup	App creation, permission request, review	Provide usernames and start the run

The Graph API is the right choice when you own the account and need first-party, permissioned access to your own business data inside a Meta-approved app. Instagram Tagged & Mentions Posts Scraper is the right choice when you need tagged/mention data for accounts you don't own or control, without waiting on app review.

Why do developers and teams scrape Instagram?

For marketers and brand teams

Brand teams use this scraper to see every post where their handle is tagged — the same visibility the Tagged tab gives a human scrolling it, but structured and exportable. A social team can pull tagged_users, caption, and is_paid_partnership for a brand handle after a campaign to confirm which creators actually tagged the account, check disclosure compliance, and pull like_count/comment_count to rank which tagged posts drove the most engagement. Because urlsOrKeywords accepts a batch of handles, a team can run the same check across a brand's regional accounts or sub-brands in one pass.

For influencer marketing and UGC teams

Influencer and partnerships teams verify sponsored placements by checking is_paid_partnership, is_affiliate, and sponsor_user on posts that tag the brand, confirming a creator actually disclosed the deal as agreed. The same feed surfaces organic user-generated content — fan posts tagging the brand with no partnership flag at all — which teams pull into a repost or UGC-licensing queue using owner.username and display_url directly from the dataset. Raising maxCommentsPerPost on a specific sponsored post also pulls its top comments, giving a team a quick read on audience reaction to a given collaboration without opening the post itself.

For AI engineers and agent builders

Because every response is typed JSON with stable field names — caption, tagged_users, latest_comments — an agent can index tagged-post data into a vector store or pass it straight into an LLM's context window without a parsing step. A brand-monitoring agent can watch mentioned_user and is_paid_partnership across a run to flag undisclosed sponsorships or new tags for human review, with no HTML scraping code inside the agent loop. maxPosts and maxCommentsPerPost let the agent control how much it ingests per account, keeping a tool call small for a quick check.

For researchers and analysts

Researchers studying tagging behavior, disclosure compliance, or engagement patterns around public accounts can pull like_count, comment_count, post_date, and tagged_users across many public accounts for a dataset that only ever touches publicly viewable posts — no private profiles, no login-gated content. That keeps the analysis within the same boundary Instagram itself applies to a logged-out visitor viewing the Tagged tab. Pulling latest_comments alongside the post data also gives a text corpus for qualitative coding of audience reaction, and the same account list can be re-run later to track how tagging volume changes over time.

How to scrape Instagram (step by step)

1. Open Instagram Tagged & Mentions Posts Scraper on its Apify Store listing and start a new run.
2. Provide urlsOrKeywords — one or more Instagram usernames or full profile URLs to check for tagged and mentioned posts.
3. Set maxPosts (10–1000 per account) and maxCommentsPerPost (1–50) to control how deep each run goes; leave proxyConfiguration on its Residential default unless you need a different proxy group.
4. Start the run and watch progress in the Apify Console log.
5. Download results as JSON, CSV, or Excel from the Dataset tab, or pull them through the Apify API.

What to do when Instagram changes its structure

Instagram Tagged & Mentions Posts Scraper is maintained, and its output field names and types stay stable even when Instagram changes its internal feed. Integrations built against this Actor's dataset schema keep working without code changes on your end.

⬇️ Input

Parameter	Required	Type	Description	Example Value
urlsOrKeywords	Yes	array	Instagram usernames (e.g. mrbeast) or full profile URLs to check for tagged/mentioned posts.	["mrbeast", "cristiano"]
maxPosts	No	integer	Maximum number of tagged posts to fetch per target account. Min 10, max 1000, default 10.	25
maxCommentsPerPost	No	integer	How many top comments to fetch per post. Min 1, max 50, default 1. Higher values slow the run down.	3
proxyConfiguration	No	object	Apify Proxy configuration. Instagram blocks non-residential IPs, so Residential is used by default.	{"useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"]}

Example input

{
  "urlsOrKeywords": ["mrbeast", "cristiano"],
  "maxPosts": 25,
  "maxCommentsPerPost": 3,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"]
  }
}

The most common input mistake is setting maxCommentsPerPost far above what you need — every extra comment fetched is an extra request per post, so raising it across a batch of high-maxPosts accounts multiplies run time quickly.

⬆️ Output

Every result is typed, normalized JSON pushed to the Apify dataset, one row per tagged/mentioned post, exportable as JSON, CSV, or Excel. Each post row nests a poster profile object, a tagged-users array, and a top-comments array inside it.

Scraped post record

{
  "post_id": "3740410677015175173_2278169415",
  "short_code": "DPom7F1kWAF",
  "post_type": "tagged",
  "is_affiliate": false,
  "is_paid_partnership": true,
  "is_ad": false,
  "like_count": 182727,
  "comment_count": 3994,
  "video_view_count": 5482837,
  "video_play_count": 5482837,
  "post_date": "2025-10-10T16:02:47.000Z",
  "display_url": "https://scontent.cdninstagram.com/...jpg",
  "alt": null,
  "caption": "Sorry Karl for pushing you into lava, but at least we rescued all the meat babies #ad #kingofmeatpartner",
  "video_duration": 62.5,
  "video_url": "https://instagram.fbrs4-2.fna.fbcdn.net/...mp4",
  "sponsor_user": [],
  "owner": {
    "id": "2278169415",
    "username": "mrbeast",
    "is_verified": true,
    "profile_pic_url": "https://instagram.fbrs4-2.fna.fbcdn.net/...jpg",
    "blocked_by_viewer": false,
    "restricted_by_viewer": null,
    "followed_by_viewer": false,
    "full_name": "MrBeast",
    "has_blocked_viewer": false,
    "is_embeds_disabled": false,
    "is_private": false,
    "is_unpublished": false,
    "requested_by_viewer": false,
    "pass_tiering_recommendation": true,
    "edge_owner_to_timeline_media": { "count": 892 },
    "edge_followed_by": { "count": 445000000 }
  },
  "latest_comments": [
    {
      "id": "18123456789",
      "text": "This was insane 😂",
      "created_at": 1728576000,
      "did_report_as_spam": false,
      "owner": {
        "id": "998877",
        "is_verified": false,
        "profile_pic_url": "",
        "username": "somefan"
      },
      "viewer_has_liked": false,
      "edge_liked_by": { "count": 12 }
    }
  ],
  "tagged_users": [
    { "user_id": "187511753", "username": "karljacobs", "full_name": "Karl Jacobs" }
  ],
  "mentioned_user": null,
  "success": true
}

Tagged user object

Every entry inside tagged_users[] describes one account tagged in that post:

{
  "user_id": "187511753",
  "username": "karljacobs",
  "full_name": "Karl Jacobs"
}

Comment object

Every entry inside latest_comments[] is one top comment on that post:

{
  "id": "18123456789",
  "text": "This was insane 😂",
  "created_at": 1728576000,
  "did_report_as_spam": false,
  "owner": {
    "id": "998877",
    "is_verified": false,
    "profile_pic_url": "",
    "username": "somefan"
  },
  "viewer_has_liked": false,
  "edge_liked_by": { "count": 12 }
}

How many results can you scrape with Instagram Tagged & Mentions Posts Scraper?

maxPosts caps tagged/mentioned posts at up to 1,000 per target account (minimum 10, default 10), and the Actor also stops paging a given account after 20 feed pages, whichever limit is hit first. maxCommentsPerPost separately caps top comments at up to 50 per post (default 1). There is no cap on how many usernames you list in urlsOrKeywords — each one is processed in turn within the same run. Pagination through the Tagged-tab feed is handled automatically using Instagram's own cursor, so you never manage after tokens yourself. No benchmark run time is published, since actual duration depends on maxPosts, maxCommentsPerPost, and how many accounts you target in one run.

Integrate Instagram Tagged & Mentions Posts Scraper and automate your workflow

Instagram Tagged & Mentions Posts Scraper works with any language or tool that can send an HTTP request, through the standard Apify API.

REST API integration

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_API_TOKEN>")

run = client.actor("api-empire/instagram-tagged-mentions-posts-scraper").call(
    run_input={
        "urlsOrKeywords": ["mrbeast", "cristiano"],
        "maxPosts": 25,
        "maxCommentsPerPost": 3,
    }
)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["short_code"], item["caption"])

Works in Python, Node.js, Go, Ruby, cURL.

Automation platforms (n8n, Make)

In n8n, call the Actor with an HTTP Request node against the Apify run endpoint, authenticated with your Apify API token, then parse the returned dataset items downstream in the workflow. In Make, the same call runs through an HTTP module (or Apify's own app if you have it connected), feeding each output post into whatever scenario you build next — a spreadsheet row, a Slack alert, or a CRM update.

Is it legal to scrape Instagram?

Yes — Instagram Tagged & Mentions Posts Scraper only collects data from public posts and public profiles; private accounts and private posts are never accessible to it. Because tagged and mentioned accounts are personal data (usernames, full names, profile pictures tied to identifiable people), any storage or downstream use of results should have a lawful basis under GDPR and comply with CCPA where applicable, particularly for bulk collection or commercial use. Consult legal counsel for commercial use cases involving bulk personal data.

Frequently asked questions

Does Instagram Tagged & Mentions Posts Scraper work without an Instagram account?

Yes. It runs fully logged-out against Instagram's public endpoints — no Instagram account, session cookie, or API key is required.

How often is the scraped data updated?

Every run fetches live data directly from Instagram's Tagged-tab feed at the moment it runs. Nothing is cached or served from a previous run.

What happens if a target account has no tagged posts?

The run completes normally and logs that the account has no publicly tagged posts right now, or that its Tagged tab is empty or private. No error is raised — the account is simply counted among the failed/empty targets in that run's summary.

Can I scrape private Instagram accounts or their tagged posts?

No. Only public accounts and public posts are accessible. Private accounts' Tagged tabs cannot be read without an authenticated session, which this Actor does not use.

Does Instagram Tagged & Mentions Posts Scraper work for AI agent workflows and LLM pipelines?

Yes. It's callable as an HTTP endpoint by any agent framework through the Apify API. Every response is typed JSON — no HTML, no selectors, no parsing step before passing results to an LLM.

How does Instagram Tagged & Mentions Posts Scraper handle Instagram's anti-bot system?

It rotates request User-Agent strings, retries failed requests up to three times with backoff, and runs behind Apify Proxy — Residential proxies by default, since Instagram blocks non-residential IPs on these endpoints.

Does Instagram Tagged & Mentions Posts Scraper return data in a format LLMs can use directly?

Yes. Every result is typed, normalized JSON with stable field names — pass it directly into an LLM context window, index it into a vector store, or route it through an agent tool without a parsing step.

Can I use Instagram Tagged & Mentions Posts Scraper without managing proxies?

Yes. Apify Proxy with a Residential group is configured by default; you never provision or rotate IPs yourself.

Does it detect posts where the account is only mentioned, not tagged?

Every result comes from the account's real Tagged-tab feed, and each one is additionally checked for an @username mention in its caption, reported in mentioned_user. A post that mentions the account in a caption but never appears in that account's Tagged tab is not returned — mention detection is a flag applied on top of the Tagged-tab feed, not a separate mentions-only crawl.

What happens when Instagram changes its structure or blocks the scraper?

Instagram Tagged & Mentions Posts Scraper is maintained, and its output schema stays stable — field names and types don't change on your end even when Instagram's internal feed does.

Your feedback

Found a bug or missing a field? We want to know. Reach out through the Actor's page on Apify or the Apify Store issue channel for this Actor, and we'll take a look. Feedback like this is what keeps the schema accurate as Instagram's own Tagged-tab feed evolves.