100 free credits. No credit card required
Naver Cafearticle Search API
Scrape Naver Cafearticle Search data with one API call. Searches cafe.naver.com. Korea's largest user-community platform (analog of Reddit subreddits / Discord servers). Returns cafe posts as PostList rows: `post.ext.title` (tags stripped, entities decoded), `post.url`, `post.content.text` (snippet), `post.author.display_name` (cafe name), and `post.author.url`. Naver publishes no date for cafe results, so rows have no `published_at` and the page says so in `_warnings` (`undated_rows`). Many cafes are member-gated; the `link` URL works only if the caller has joined the cafe (Naver returns a teaser otherwise).
Last updated October 2026Maintained by the SocialCrawl team
Returns posts from Naver Cafe communities: post title, link, a text snippet, and the name and URL of the cafe that hosts it. Naver sends no post date for cafe results.
Use it for Korean community discussion; many cafes are members only, so a link may show only a teaser.
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 SearchNaver API
What can you do with the Cafearticle Search API?
The Cafearticle Search endpoint gives you structured Naver 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/naver/cafearticle/search?query=%EC%A3%BC%EC%8B%9D&display=10&label=sponsored&relevance=filter"import requests
response = requests.get(
"https://www.socialcrawl.dev/v1/naver/cafearticle/search",
params={
'query': '주식',
'display': '10',
'label': 'sponsored',
'relevance': 'filter',
},
headers={"x-api-key": "YOUR_API_KEY"},
)
data = response.json()const response = await fetch(
"https://www.socialcrawl.dev/v1/naver/cafearticle/search?query=%EC%A3%BC%EC%8B%9D&display=10&label=sponsored&relevance=filter",
{
headers: { "x-api-key": "YOUR_API_KEY" },
},
);
const data = await response.json();Parameters worth sending
label=sponsoredMarks each post that is paid or sponsored, at no extra credits; intent and niche are free too.relevance=filterDrops rows that are not about your query, at no extra credits; relevance=score keeps them and adds a score.
Parameters
| Parameter | Required | Description |
|---|---|---|
| query | Yes | Free-text search term (UTF-8). Required. Search short noun phrases (for example `다한증` or `다한증 수술`), which match best: Naver does not treat quotes as an exact phrase. Add `relevance=filter` to drop the rows that are not about the query. |
| display | No | Number of items to return per page. Defaults to 10, cap 100; `display=101` returns 400. |
| start | No | 1-indexed offset for pagination. Defaults to 1, cap 1000; `start=1001` returns 400. Past rank 100, `sim` and `point` pages repeat Naver's top 100: those rows are removed and counted in `_warnings` (`recycled_rows`), and an emptied page is free. Page deeper with `sort=date`. |
| sort | No | Sort order. Accepted values: sim|date. Defaults to `sim` (relevance) when the corpus supports sort. Values outside this corpus's domain are rejected with a 400 before any upstream call. `date` lists the newest first, but Naver serves only the first 1,000 rows of a query (`start` caps at 1000), so on a common word it reaches back hours or days, not months, and on a multi-word query Naver also loosens the match. Cafe rows carry no date, so `since=` cannot narrow them. (sim | date) |
| relevance | No | Optional. 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_threshold | No | Optional, 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_to | No | Optional, 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. |
| 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) |
| 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. |
| label | No | Optional 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. |
| exclude | No | Optional 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. |
| brand | No | Required 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_description | No | Optional, 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. |
| offer | No | Optional, 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_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) |
| 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 Naver Cafearticle Search 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": "naver",
"endpoint": "/v1/naver/cafearticle/search",
"data": {
"items": [
{
"post": {
"id": "https://cafe.naver.com/divclub/64226",
"url": "https://cafe.naver.com/divclub/64226",
"content": {
"text": "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": null,
"display_name": "user_9f7e4c",
"avatar_url": null,
"verified": null
},
"engagement": {
"views": null,
"likes": null,
"comments": null,
"shares": null,
"saves": null
},
"flags": {
"nsfw": null,
"spoiler": null,
"pinned": null,
"deleted": false
},
"published_at": null
},
"computed": {
"engagement_rate": null,
"language": "ko",
"content_category": "other",
"estimated_reach": null
}
},
{
"post": {
"id": "https://cafe.naver.com/divclub/64087",
"url": "https://cafe.naver.com/divclub/64087",
"content": {
"text": "Sample comment text (redacted).",
"media_urls": null,
"thumbnail_url": null,
"duration_seconds": null
},
"author": {
"username": null,
"display_name": "user_9f7e4c",
"avatar_url": null,
"verified": null
},
"engagement": {
"views": null,
"likes": null,
"comments": null,
"shares": null,
"saves": null
},
"flags": {
"nsfw": null,
"spoiler": null,
"pinned": null,
"deleted": false
},
"published_at": null
},
"computed": {
"engagement_rate": null,
"language": "ko",
"content_category": "other",
"estimated_reach": null
}
}
],
"total": 35063414,
"dropped": 0
},
"credits_used": 1,
"request_id": "req_example000000",
"cached": false,
"pagination": {
"next_cursor": "sc.eyJ2IjoyLCJjIjoiMTEiLCJlIjoibmF2ZXIvY2FmZWFydGljbGUvc2VhcmNoIiwicCI6InN0YXJ0In0",
"has_more": true,
"page_size": 10
},
"credits_remaining": 9999,
"source": "captured",
"captured_at": "2026-10-02T12:40:52.841Z",
"redacted": true
}Live sample illustrating the unified response shape. Field values reflect the record you query.
API Details
How does the Naver Cafearticle Search 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"
}
}
}FAQ
Have a question? We got answers
Find answers to frequently asked questions about SocialCrawl's API, pricing, and capabilities.
Contact usHow do I search Naver Cafe articles with an API?
Send a GET request to /v1/naver/cafearticle/search with your keyword in the query parameter. SocialCrawl searches cafe.naver.com community posts and returns title, link, description, cafe name, and cafe URL under data.items.
What is Naver Cafe and why search it?
Naver Cafe is Korea's largest community platform, the closest analog to Reddit subreddits or Discord servers. Searching it surfaces grassroots Korean discussion on products, hobbies, and local topics that does not appear on the open web.
Can I always open a Naver Cafe article from the link?
Not always. Many cafes are member-gated, so the returned link is a teaser preview unless you have joined that cafe. The title, description, and cafe name still come back in the response, which is enough for discovery and trend tracking.
What sort options does the Naver Cafe endpoint support?
sort accepts sim (relevance, the default) and date (newest first). Use display and start for pagination. Combine sort=date with regular polling to track fresh community discussion on a keyword across Naver cafes.
How much does the Naver Cafe API cost?
Each Naver Cafe search costs 1 credit on the standard tier, no matter how many articles match. New accounts get 100 free credits with no credit card required, enough to run roughly 100 community searches before any charge.
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