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LinkedIn Search Posts API

API 한 번의 호출로 LinkedIn Search Posts 데이터를 받아 가세요. Finds public LinkedIn posts, feed updates, and Pulse articles via Google Search. Returns description, author, media, like count, comment count, and published date when LinkedIn exposes them publicly. Best-effort against Google's index, not a complete native LinkedIn search. Use `date_posted` to narrow to recent posts and `page` to walk deeper. **Many posts in one call.** Pass `limit` (1 to 200) with a `query` and the call reads as many pages as it needs, removes duplicates, and returns up to that many posts in relevance order, charged 1 credit for every 5 posts returned, so 200 posts is 40 credits. Repeat the identical call inside the 2-minute cache window and it is served from cache for nothing. The response carries a `walk` summary saying how many pages were read and why the call stopped. **Reading one member's back catalogue.** Pass `from_member` on its own, with no `query`, and page through the result: this is the only way to reach a member's posts older than the 100 that /v1/linkedin/profile/posts can return, and unlike that endpoint it hands you a real `next_cursor` that keeps advancing. Get the urn from `author.ext.urn` on /v1/linkedin/profile. Those results come back newest-first; `sort_by=relevance` is rejected on a subject-only call because there is no query for them to be relevant to. **This is not a complete history, and how much it returns varies by member.** It reads a public search index, so it holds whatever that index holds for a given profile and no more. Measured 06/09/2026: one prolific member exhausted at 395 posts, while a customer walking his own profile of 800+ posts reached 223. Neither number is a limit you can raise by paging harder; the walk simply ends when the index runs out. Treat the depth as unknown until you walk it, and do not design around a fixed figure. For a member whose /v1/linkedin/profile/posts call already returns fewer posts than the `limit` you asked for, that feed is their whole history and this endpoint returns less, not more.

2026년 10월 업데이트SocialCrawl 팀이 직접 관리해요

무료로 시작하기문서 보기

키워드에 맞는 LinkedIn 공개 게시물과 Pulse 아티클을 반환합니다. 본문과 작성자, 미디어, 좋아요·댓글 수, 날짜가 나옵니다.

키워드를 폭넓게 모니터링할 때 사용하세요. 공개 검색 결과를 읽는 방식이라 수집 범위는 완전하다기보다 최선에 가깝습니다.

68개 플랫폼을 병렬로 검색합니다

·TikTok logoTikTok·Instagram logoInstagram·YouTube logoYouTube·Facebook logoFacebook·X logoX·LinkedIn logoLinkedIn·Reddit logoReddit·Threads logoThreads·Pinterest logoPinterest·Twitch logoTwitch·Truth Social logoTruth Social·Snapchat logoSnapchat·Kick logoKick·Bluesky logoBluesky·Kwai logoKwai·Rumble logoRumble·Spotify logoSpotify·Apple Music logoApple Music·TikTok Shop logoTikTok Shop·Amazon Shop logoAmazon Shop·Google Shopping logoGoogle Shopping·Trustpilot logoTrustpilot·TripAdvisor logoTripAdvisor·Yelp logoYelp·Linktree logoLinktree·Komi logoKomi·Pillar logoPillar·lnk.bio logolnk.bio·Facebook Ads logoFacebook Ads·Google Ads logoGoogle Ads·LinkedIn Ads logoLinkedIn Ads·Google Search logoGoogle Search·Google News logoGoogle News·Finance logoFinance·Polymarket logoPolymarket·Tavily logoTavily·Hacker News logoHacker News·GitHub logoGitHub·Perplexity logoPerplexity·Naver logoNaver·Utility logoUtility·Universal Search logoUniversal Search
·TikTok logoTikTok·Instagram logoInstagram·YouTube logoYouTube·Facebook logoFacebook·X logoX·LinkedIn logoLinkedIn·Reddit logoReddit·Threads logoThreads·Pinterest logoPinterest·Twitch logoTwitch·Truth Social logoTruth Social·Snapchat logoSnapchat·Kick logoKick·Bluesky logoBluesky·Kwai logoKwai·Rumble logoRumble·Spotify logoSpotify·Apple Music logoApple Music·TikTok Shop logoTikTok Shop·Amazon Shop logoAmazon Shop·Google Shopping logoGoogle Shopping·Trustpilot logoTrustpilot·TripAdvisor logoTripAdvisor·Yelp logoYelp·Linktree logoLinktree·Komi logoKomi·Pillar logoPillar·lnk.bio logolnk.bio·Facebook Ads logoFacebook Ads·Google Ads logoGoogle Ads·LinkedIn Ads logoLinkedIn Ads·Google Search logoGoogle Search·Google News logoGoogle News·Finance logoFinance·Polymarket logoPolymarket·Tavily logoTavily·Hacker News logoHacker News·GitHub logoGitHub·Perplexity logoPerplexity·Naver logoNaver·Utility logoUtility·Universal Search logoUniversal Search
·TikTok logoTikTok·Instagram logoInstagram·YouTube logoYouTube·Facebook logoFacebook·X logoX·LinkedIn logoLinkedIn·Reddit logoReddit·Threads logoThreads·Pinterest logoPinterest·Twitch logoTwitch·Truth Social logoTruth Social·Snapchat logoSnapchat·Kick logoKick·Bluesky logoBluesky·Kwai logoKwai·Rumble logoRumble·Spotify logoSpotify·Apple Music logoApple Music·TikTok Shop logoTikTok Shop·Amazon Shop logoAmazon Shop·Google Shopping logoGoogle Shopping·Trustpilot logoTrustpilot·TripAdvisor logoTripAdvisor·Yelp logoYelp·Linktree logoLinktree·Komi logoKomi·Pillar logoPillar·lnk.bio logolnk.bio·Facebook Ads logoFacebook Ads·Google Ads logoGoogle Ads·LinkedIn Ads logoLinkedIn Ads·Google Search logoGoogle Search·Google News logoGoogle News·Finance logoFinance·Polymarket logoPolymarket·Tavily logoTavily·Hacker News logoHacker News·GitHub logoGitHub·Perplexity logoPerplexity·Naver logoNaver·Utility logoUtility·Universal Search logoUniversal Search
·TikTok logoTikTok·Instagram logoInstagram·YouTube logoYouTube·Facebook logoFacebook·X logoX·LinkedIn logoLinkedIn·Reddit logoReddit·Threads logoThreads·Pinterest logoPinterest·Twitch logoTwitch·Truth Social logoTruth Social·Snapchat logoSnapchat·Kick logoKick·Bluesky logoBluesky·Kwai logoKwai·Rumble logoRumble·Spotify logoSpotify·Apple Music logoApple Music·TikTok Shop logoTikTok Shop·Amazon Shop logoAmazon Shop·Google Shopping logoGoogle Shopping·Trustpilot logoTrustpilot·TripAdvisor logoTripAdvisor·Yelp logoYelp·Linktree logoLinktree·Komi logoKomi·Pillar logoPillar·lnk.bio logolnk.bio·Facebook Ads logoFacebook Ads·Google Ads logoGoogle Ads·LinkedIn Ads logoLinkedIn Ads·Google Search logoGoogle Search·Google News logoGoogle News·Finance logoFinance·Polymarket logoPolymarket·Tavily logoTavily·Hacker News logoHacker News·GitHub logoGitHub·Perplexity logoPerplexity·Naver logoNaver·Utility logoUtility·Universal Search logoUniversal Search
LinkedIn API

Search Posts API로 무엇을 할 수 있을까요

Search Posts 엔드포인트가 통합 스키마와 계산 필드를 담은 LinkedIn 데이터를 한 번의 요청으로 보내드려요. 스크래핑 인프라를 직접 만들거나 유지할 필요가 없어요.

요청 예시

curl -H "x-api-key: YOUR_API_KEY" \
  "https://www.socialcrawl.dev/v1/linkedin/search/posts?query=ai+agents&date_posted=past_week&label=sponsored&relevance=filter"
import requests

response = requests.get(
    "https://www.socialcrawl.dev/v1/linkedin/search/posts",
    params={
    'query': 'ai agents',
    'date_posted': 'past_week',
    'label': 'sponsored',
    'relevance': 'filter',
    },
    headers={"x-api-key": "YOUR_API_KEY"},
)

data = response.json()
const response = await fetch(
  "https://www.socialcrawl.dev/v1/linkedin/search/posts?query=ai+agents&date_posted=past_week&label=sponsored&relevance=filter",
  {
    headers: { "x-api-key": "YOUR_API_KEY" },
  },
);

const data = await response.json();

함께 보내면 좋은 파라미터

  • label=sponsored 게시물마다 유료 광고나 협찬인지 표시합니다. 추가 크레딧은 들지 않으며 의도와 분야 판정도 무료입니다.
  • relevance=filter 검색어와 관련 없는 결과를 제외합니다. 추가 크레딧은 들지 않습니다. relevance=score를 보내면 결과를 모두 남기고 관련도 점수를 붙입니다.
  • date_posted=past_week 이 기간에 올라온 게시물만 반환합니다.

파라미터

파라미터필수설명
page아니오1-based page number of Google results. Defaults to 1.
sort_by아니오Result ordering: `relevance` (default) or `date_posted`. On a subject-only call (`from_member` or `from_company` with no `query`) the results are ordered by date and `relevance` is rejected, because there is no query for them to be relevant to. (date_posted | relevance)
date_posted아니오Date filter based on Google-indexed results. One of `past_24h`, `past_week`, `past_month` (underscores, not hyphens). Note the JOB lanes use `past_24_hours` for the same concept; the two vocabularies are not interchangeable. (past_24h | past_week | past_month)
content_type아니오Narrow to one kind of post. (videos | photos | jobs | live_videos | documents | collaborative_articles)
from_company아니오Filter to posts from a company, by numeric LinkedIn company id (for example `1035`). A company slug or URL is rejected; read the id from `author.id` on /v1/linkedin/company.
from_member아니오Filter to posts from one member, by their LinkedIn member urn — the bare `ACoAA…` value published as `author.ext.urn` by /v1/linkedin/profile. A profile URL, a public slug (`williamhgates`) or a `urn:li:fsd_profile:`-prefixed value is rejected, because the source cannot use any of them. Pass this on its own, with no `query`, to walk one member's back catalogue.
query아니오Keyword or phrase to search for. Optional when `from_member` or `from_company` is set: omit it to get that subject's posts unfiltered, and note that supplying a real content word narrows the results to posts matching it.
limit아니오Return up to this many posts in one call, 1 to 200. The call reads as many pages as it needs, removes duplicates, and is charged 1 credit for every 5 posts returned (200 posts = 40 credits). Repeating the identical call inside the 2-minute cache window is free. Requires `query`. Cannot be combined with `page` or `cursor`: the result always starts at the top and `pagination.next_cursor` is null. `pagination.has_more` says whether more posts were available when the call stopped, and `walk.stopped` says why it stopped (`limit`, `exhausted`, `deadline` or `sources_unavailable`).
relevance아니오Optional. Without this param every row already carries computed.relevance against your query, free ({ p, sense, depth, spam }: is this row about what your query means, or a different thing that shares its words?), and nothing is dropped or reordered. score asks for it explicitly and waits for every row; filter also drops the rows that are not about your query, and lists their ids in data.relevance.dropped_ids. Your query is the topic; nothing to configure. A row that could not be judged is never dropped and carries relevance: null. Pagination is unchanged, so a filtered page can hold fewer rows. Free with your query as the topic; with relevant_to it adds 1 credit per started 25 newly judged rows (rows already judged for the same topic are free, and so is a cached page). (score | filter)
relevance_threshold아니오Optional, only with relevance. The probability (0 to 1) a row must reach to be kept by relevance=filter. Default 0.5. Lower keeps more rows, higher keeps fewer.
relevant_to아니오Optional, only with relevance. Up to 200 characters describing what you mean, used as the topic instead of the query. Use it when the query is ambiguous, for example query=cleopatra with relevant_to=Cleopatra, the IGT slot game.
judgments아니오Optional, 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_run아니오Optional. 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)
fit아니오Optional. 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)
goal아니오Required by fit=goal. What you are trying to do, in your own words, up to 300 characters.
fit_tokens아니오Optional, 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.
label아니오Optional CSV of SocialCrawl labels to add to every post. Without this param every page already carries sponsored, intent and niche, free; label= adds the labels you name to them (the defaults keep running). sponsored, intent and niche are free when asked for too; mention, quality, intent with offer=, and injection add 1 credit per started 25 newly judged posts. judgments=off (or label=none) turns the default labels off. sponsored: is the post a paid or gifted promotion (p, 0 to 1), did it carry a disclosure marker such as #ad or 광고 (disclosed), is it likely paid with no marker (undisclosed), and which of the accounts it mentions does it promote (brand, or null). These are signals to review, never a finding. mention (needs brand=): is the post about that brand rather than something that shares its name (about_brand, 0 to 1), how it feels about the brand on five levels (sentiment_level 0 to 4 and sentiment_score 0 to 1, null when the post is not about the brand), is it sarcastic, which aspect it talks about (taste_or_quality, price_or_value, availability_or_delivery, health_or_safety, advertising_or_campaign, customer_service, none), and did the author buy or use it (first_hand). intent: what the author is mainly doing (label: asking_for_recommendation, comparing_options, switching_away, complaining, promoting, news_or_discussion, other, or null when unsure, with confidence), whether they read as a potential buyer rather than a seller (buyer, seller), how pressing the need is (urgency, 0 to 3), and, when you pass offer=, whether your offer would plausibly help them (fits_offer). niche: which of the 33 niches of the published taxonomy sc-niche-v1 the caption belongs to, or personal_no_niche, or other (label, confidence, taxonomy), with label null when the caption is too thin to tell or the pick is unsure. It reads the caption only, not the video. quality: how much checkable detail the caption carries (fact_density 0 to 3), whether it mainly asks for likes, replies, shares, follows or tags (engagement_bait, 0 to 1), whether it is written to provoke anger as a way to get engagement (rage_bait, 0 to 1; about the writing, never the side taken), whether it only repeats someone else's news or view (secondhand, 0 to 1), and what the post is mainly doing (post_aim: inform, opinion, sell, entertain, provoke, other). exclude=engagement_bait drops posts whose engagement_bait is 0.8 or higher. injection flags text that addresses an AI system and tries to direct it (flagged, p); it never drops or rewrites a row. A post that could not be judged carries labels: null. Posts already labelled for anyone are free, and so is a cached page. data.labels reports what was judged and billed.
exclude아니오Optional CSV (engagement_bait), only with label=quality. Drops posts whose engagement_bait probability is 0.8 or higher and lists their ids in data.labels.dropped_ids. A post that could not be judged is never dropped.
brand아니오Required by label=mention, ignored otherwise. The brand or product the posts are judged against, up to 300 characters, for example brand=Buldak. Without it label=mention is skipped with the warning label_mention_needs_brand and is not billed.
brand_description아니오Optional, with label=mention. One plain sentence saying what the brand is, for example brand_description=Samyang's spicy instant noodle brand. Use it when the name is also an ordinary word or another company's name.
offer아니오Optional, with label=intent. One or two plain sentences saying what you sell, up to 300 characters, for example offer=A web design agency that builds websites for small businesses. Adds fits_offer to every post; without it fits_offer is null.
label_evidence아니오Optional, 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)
since아니오Only 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`.
until아니오Only 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`.
응답 예시

LinkedIn Search Posts API는 무엇을 돌려주나요

모든 응답은 하나의 통합 스키마를 따라요. 크레딧을 쓰기 전에 어떤 필드가 돌아오는지, 실제 응답 본문 그대로 확인해 보세요.

응답 예시 보기
{
  "success": true,
  "platform": "linkedin",
  "endpoint": "/v1/linkedin/search/posts",
  "data": {
    "items": [
      {
        "post": {
          "id": "7510831414699933696",
          "url": "https://www.linkedin.com/feed/update/urn:li:activity:7510831414699933696",
          "content": {
            "text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted).",
            "media_urls": null,
            "thumbnail_url": null,
            "duration_seconds": null
          },
          "author": {
            "username": "user_c16612",
            "display_name": "user_222f59",
            "avatar_url": "https://example.com/avatars/user_5e45ac.png",
            "verified": null
          },
          "engagement": {
            "views": null,
            "likes": 0,
            "comments": 0,
            "shares": 0,
            "saves": null
          },
          "flags": {
            "nsfw": null,
            "spoiler": null,
            "pinned": null,
            "deleted": false
          },
          "published_at": "2026-09-29T22:42:33.764Z",
          "ext": {
            "reaction_counts": [],
            "author_headline": "user_1634e3"
          }
        },
        "computed": {
          "engagement_rate": null,
          "language": "en",
          "content_category": "travel",
          "estimated_reach": null,
          "relevance": {
            "p": 0.96,
            "sense": "target",
            "depth": 3,
            "spam": 0.1
          },
          "labels": {
            "sponsored": {
              "p": 0.02,
              "disclosed": false,
              "undisclosed": false,
              "brand": null
            },
            "intent": {
              "label": "news_or_discussion",
              "confidence": 1,
              "buyer": false,
              "seller": 0.18,
              "fits_offer": null,
              "urgency": 0.26
            },
            "niche": {
              "label": "technology",
              "confidence": 1,
              "taxonomy": "sc-niche-v1"
            }
          }
        }
      },
      {
        "post": {
          "id": "7510861527219044352",
          "url": "https://www.linkedin.com/feed/update/urn:li:activity:7510861527219044352",
          "content": {
            "text": "Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted). Sample comment text (redacted).",
            "media_urls": null,
            "thumbnail_url": null,
            "duration_seconds": null
          },
          "author": {
            "username": "user_d9c51e",
            "display_name": "user_f468d7",
            "avatar_url": "https://example.com/avatars/user_82b488.png",
            "verified": null
          },
          "engagement": {
            "views": null,
            "likes": 7,
            "comments": 13,
            "shares": 2,
            "saves": null
          },
          "flags": {
            "nsfw": null,
            "spoiler": null,
            "pinned": null,
            "deleted": false
          },
          "published_at": "2026-09-30T00:42:13.148Z",
          "ext": {
            "reaction_counts": [
              {
                "count": 7,
                "type": "LIKE"
              }
            ],
            "author_headline": "user_89064d"
          }
        },
        "computed": {
          "engagement_rate": null,
          "language": "en",
          "content_category": "other",
          "estimated_reach": null,
          "relevance": {
            "p": 0.84,
            "sense": "target",
            "depth": 1.07,
            "spam": 0.05
          },
          "labels": {
            "sponsored": {
              "p": 0.02,
              "disclosed": false,
              "undisclosed": false,
              "brand": null
            },
            "intent": {
              "label": "news_or_discussion",
              "confidence": 1,
              "buyer": false,
              "seller": 0.2,
              "fits_offer": null,
              "urgency": 1.54
            },
            "niche": {
              "label": "technology",
              "confidence": 0.99,
              "taxonomy": "sc-niche-v1"
            }
          }
        }
      }
    ],
    "walk": {
      "requested": 2,
      "returned": 2,
      "pages_attempted": 1,
      "pages_served": 0,
      "duplicates_removed": 0,
      "stopped": "limit"
    },
    "relevance": {
      "mode": "score",
      "topic_source": "query",
      "rows": 2,
      "scored": 2,
      "cached": 0,
      "kept": 2,
      "dropped": 0,
      "dropped_ids": [],
      "unjudged": 0,
      "too_short": 0,
      "threshold": 0.5,
      "extra_credits": 0,
      "methodology_version": "relevance/2",
      "origin": "default",
      "pending": 0,
      "pending_ids": [],
      "status": "complete"
    },
    "labels": {
      "presets": [
        "sponsored",
        "intent"
      ],
      "rows": 2,
      "labelled": 2,
      "cached": 0,
      "skipped": 0,
      "unjudged": 0,
      "dropped": 0,
      "dropped_ids": [],
      "extra_credits": 0,
      "methodology_version": "labels/1",
      "mode": "default",
      "status": "complete",
      "pending": 0,
      "pending_ids": []
    },
    "dropped": 0
  },
  "credits_used": 1,
  "request_id": "req_example000000",
  "cached": false,
  "pagination": {
    "next_cursor": null,
    "has_more": true,
    "page_size": 2
  },
  "credits_remaining": 9999,
  "source": "captured",
  "captured_at": "2026-10-02T07:31:50.586Z",
  "redacted": true
}

통합 응답 구조를 보여 주는 실제 샘플이에요. 필드 값은 조회한 대상에 따라 달라져요.

API 상세

LinkedIn Search Posts API는 어떻게 동작하나요

API 키와 함께 GET 요청을 보내면, 통합 스키마와 계산 필드를 담은 깔끔한 JSON이 돌아와요.

메서드

GET

응답 형식

JSON

왜 SocialCrawl인가요

LinkedIn Search Posts 데이터는 왜 SocialCrawl로 가져올까요

LinkedIn 데이터 추출에서 까다로운 부분은 SocialCrawl이 가려 드려요. 통합 스키마, AI 인리치먼트, 그리고 코드에 남지 않는 플랫폼 로직까지.

개발자 우선

소셜 미디어 데이터를 몇 초 만에 수집하는 방법

개발자를 위한 가장 빠른 소셜 미디어 스크래핑 API. 월간 활성 사용자 100억 명 이상을 포괄하는 68개 플랫폼에서 프로필, 게시물, 댓글, 분석 데이터를 수집하세요.

모든 플랫폼을 하나의 스키마로

동일한 응답 구조로 68개 플랫폼을 조회하세요. 연동은 한 번이면 충분합니다.

단순 수집을 넘어 계산된 필드 제공

엔드포인트가 해당 지표를 지원하고 계산에 필요한 원본 값이 있을 때, 정규화된 레코드에 engagement_rate, estimated_reach, content_category, language를 함께 담아 바로 활용할 수 있습니다.

코드 한 줄 쓰기 전에, 데이터부터

Visual Data Explorer에 URL만 붙여넣으면 결과 카드와 정형화된 테이블, CSV 내보내기를 바로 사용할 수 있습니다.

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"
    }
  }
}
+ 68개 플랫폼
자주 묻는 질문

자주 묻는 질문

API, 요금제, 기능에 대한 질문과 답변입니다.

문의하기
공개 LinkedIn 게시물은 API로 어떻게 검색하나요?
/v1/linkedin/search/posts에 GET 요청을 보내고 query에 검색어를 넣어주세요. Google 색인을 통해 공개 게시물, 피드 업데이트, Pulse 아티클을 찾아 작성자, 미디어, 좋아요 수, 댓글 수, 게시일과 함께 돌려드려요.
네이티브 검색인가요, 베스트에포트인가요?
Google 색인 기반 베스트에포트예요. LinkedIn 자체의 완전한 검색은 아니에요. Google이 색인한 공개 게시물과 Pulse 아티클을 찾아주기 때문에 범위는 넓지만 모든 게시물을 포함한다고 보장하지는 않아요.
게시물 검색은 어떤 필터를 지원하나요?
query 외에 date_posted, sort_by, content_type, from_company, from_member를 더할 수 있고 page로 페이지네이션해요. from_member에는 회원 urn을 넣어주세요. /v1/linkedin/profile 응답의 author.ext.urn에 있는 ACoAA... 값이에요. from_company에는 숫자로 된 회사 id를 넣으시면 돼요. 둘 중 하나만 넣고 query를 비우면 해당 계정이 올린 글을 페이지 단위로 계속 넘겨볼 수 있어요. /v1/linkedin/profile/posts가 돌려주는 100건보다 오래된 글은 이렇게 가져오시면 돼요. 다만 공개 검색 색인을 읽는 방식이라 전체 기록은 아니에요. 어디까지 거슬러 올라가는지는 계정마다 달라서, 직접 넘겨보며 확인하시고 특정 건수를 전제로 설계하지는 말아 주세요.
LinkedIn Post Search API 요금은 얼마인가요?
페이지당 5 크레딧(어드밴스드 티어)이에요. 몇 건이 돌아오든 과금은 같고, 신규 계정은 신용카드 없이 100 크레딧을 무료로 받아 20번까지 검색하며 범위를 확인할 수 있어요.
각 게시물은 어떤 필드로 오나요?
게시물이 공개된 경우 설명, 작성자, 미디어, 좋아요 수, 댓글 수, 게시일을 받아볼 수 있어요. Google 색인이 노출하지 않는 필드는 비어 있을 수 있어서 수치는 참고용으로 봐주세요.
게시물 검색 결과는 어떻게 페이지네이션하나요?
page 쿼리 파라미터(정수)를 하나씩 늘리면 더 많은 결과를 받아볼 수 있어요. page=1부터 시작해 빈 목록이 돌아오면 색인된 결과의 끝이에요.

AI에게 SocialCrawl을 물어보세요

ChatGPT에 물어보기Perplexity에 물어보기Gemini에 물어보기Claude에 물어보기Grok에 물어보기

LinkedIn Search Posts 데이터, 가져올 준비 되셨어요?

API 키 받고 60초 안에 LinkedIn 데이터를 받아 가세요.

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