Naver Café Scraper (네이버 카페)

Scrape public Naver Café content in one run — article lists, full post bodies, and comment threads. Give the Actor a café (URL, café name, or clubId), an optional board or search keyword, and it returns the café's metadata (name, member count, manager, board list) plus its articles — each with title, board, author, member level, date, view/comment/like counts, full body text, and (optionally) the entire comment thread. Point it at a direct article link instead and it reads just that post. Built for community monitoring, brand & keyword tracking, and market research in the Korean market.

Because it runs on the Apify platform, you get scheduled runs, a REST API, webhooks and integrations (Google Sheets, Make, Zapier, Slack…), automatic proxy rotation through Korean IPs, and monitoring — no servers to manage.

Why use Naver Café Scraper?

• Community monitoring — track a café or a single board (게시판) for new posts, with author, timestamp and engagement on every article.
• Brand & keyword tracking — use the in-café search keyword to pull every article mentioning your brand or product.
• Market & competitor research — Naver Cafés are where Korean hobby, product and fan communities actually talk; export the discussion as structured data.
• Sentiment & comment analysis — pull full comment threads (with reply structure and member levels) for any article.
• Bulk collection — feed many cafés or article links and get one tidy dataset.

How to use Naver Café Scraper

1. Enter one or more cafés or article links — a café name (steamindiegame), a numeric clubId (27842958), a café URL (https://cafe.naver.com/steamindiegame), or a direct article link (https://cafe.naver.com/steamindiegame/21892615). A café link scrapes its article list; an article link reads just that post.
2. (Optional) Set a board (menuId) to restrict listing to one board, or a search keyword to pull matching articles instead of the recent feed.
3. (Optional) Set Max articles per café to cap collection — defaults to 5 for a quick, cheap first run; set 0 for no cap.
4. (Optional) Turn on Include comments to pull each article's comment thread.
5. Click Start. When the run finishes, open the Output tab and download the data as JSON, CSV, Excel, or HTML — or pull it from the API.

Input

Configure the run in the Input tab (or via JSON / API). Key fields:

Field	Description	Default
cafes	Café names, clubIds, café URLs, or article links — one per line. Required.	—
menuId	Restrict listing to one board by its Naver menuId. 0 = recent across the whole café.	0
keyword	In-café search keyword. When set, articles come from search results, not the recent feed.	— (empty)
maxArticles	Cap articles collected per café (newest first). 0 = no cap.	5
includeArticleContent	Fetch each article's full body (plain text and HTML).	true
includeComments	Fetch each article's comment thread (nested under the article).	false
maxCommentsPerArticle	Cap comments per article. 0 = all.	0
includeMenus	Attach the café's board/menu tree (find menuIds here).	true
outputMode	articleItems = one item per article (+ a café item); cafeItem = one item per café, nested.	articleItems
language	Language preference sent to Naver. Korean is strongly recommended.	ko
maxConcurrency	Cafés processed in parallel.	3
proxyConfiguration	Proxy for every request. Korean residential IPs recommended.	Apify Proxy, RESIDENTIAL, KR

Example input:

{
    "cafes": ["https://cafe.naver.com/steamindiegame"],
    "keyword": "신작",
    "maxArticles": 100,
    "includeArticleContent": true,
    "includeComments": false,
    "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"], "apifyProxyCountry": "KR" }
}

Output

Results are written to the dataset in one of two shapes, chosen by outputMode:

• articleItems (default) — one dataset item per article ("type": "article", each tagged with its café's clubId and cafeName), plus one café item per café ("type": "cafe"). Best for spreadsheets and per-article analysis.
• cafeItem — one item per café ("type": "cafe") with all articles nested under an articles array. Best when you want a café and its whole harvest as a single object.

You can download the dataset in various formats such as JSON, HTML, CSV, or Excel.

Example café item:

{
    "type": "cafe",
    "clubId": "27842958",
    "cafeUrl": "steamindiegame",
    "cafeName": "왁물원 :: 종합 거시기 스트리머 우왁굳 팬카페",
    "url": "https://cafe.naver.com/steamindiegame",
    "memberCount": 567761,
    "managerNickname": "parable",
    "openType": "O",
    "menuCount": 766,
    "menus": [{ "menuId": 1, "menuName": "💩 자유 게시판", "menuType": "B", "boardType": "L" }]
}

Example article item (content present only when includeArticleContent is on; comments only when includeComments is on):

{
    "type": "article",
    "clubId": "27842958",
    "cafeName": "왁물원",
    "articleId": "21892645",
    "url": "https://cafe.naver.com/steamindiegame/21892645",
    "menuId": 1952,
    "menuName": "자동차 게시판",
    "title": "\"갤로퍼의 후계자\" 현대 신형 '볼더' 양산되나",
    "writerNickname": "징 버 거",
    "memberLevel": 140,
    "writeDate": "2026-07-21T12:34:19.723Z",
    "readCount": 1,
    "commentCount": 0,
    "likeCount": 0,
    "tags": [],
    "content": "🚘 현대차 정통 오프로더 '볼더(Boulder)' 출원 소식 …",
    "contentLength": 503,
    "comments": []
}

Data fields

Café

Field	Description
clubId	Numeric Naver café id.
cafeUrl / url	Café vanity name and full URL.
cafeName	Café title.
memberCount	Number of members.
managerId / managerNickname	Café manager (운영자).
openType	"O" = open café.
menus	Board tree with menuId + name.

Each article

Field	Description
articleId	Naver's article id.
url	Link to the article.
menuId / menuName	The board it was posted in.
title	Article title.
writerNickname / memberLevel	Author nickname and café member level.
writeDate	Publish time (ISO 8601).
readCount	View count.
commentCount / likeCount	Comment and like (좋아요) counts.
tags	Article tags.
content / contentLength	Full body text + length (opt-in).
contentHtml	Original post HTML, as published (opt-in).
comments	Comment thread (opt-in) — see below.

Each comment (when includeComments is on)

Field	Description
commentId / parentId	Comment id and parent (for replies).
isReply	Whether it's a reply to another comment.
writerNickname / memberLevel	Commenter nickname and level.
content / stickerUrl	Comment text and/or sticker image.
date	Comment time (ISO 8601).

How much does it cost to scrape Naver Café?

This Actor uses pay-per-event pricing, so you pay for the results you get — not for time or servers. You're charged per article collected, with a small additional charge when you also pull an article's full body or its comment thread. Cost scales with what you collect: keep spend predictable with Max articles per café, or collect just list metadata by turning full content off. See the Pricing tab for current rates; the Apify free tier is enough to try it out.

Tips & advanced options

• Target one board: find its menuId in a café record's menus list, then set the Board (menuId) input.
• Keyword monitoring: set a search keyword and schedule the Actor to re-run — you get a rolling feed of new mentions.
• Cap busy cafés: large cafés post constantly; set Max articles per café to keep runs fast and predictable.
• Metadata-only, fast & cheap: turn Include full article content off to collect just list metadata (title, author, date, counts).
• Use Korean residential proxies: Naver serves the most complete content to Korean IPs. The default (Apify Proxy, RESIDENTIAL, country KR) is strongly recommended for reliable results.

FAQ, disclaimers & support

Is scraping Naver Café legal? This Actor collects only publicly available café content — what anyone can view in a browser without logging in. You are responsible for how you use the data, including compliance with Naver's Terms of Service and applicable laws (such as GDPR / PIPA) when handling any personal data. Do not use it to collect private or sensitive information.

Why are some articles missing or bodyless? Naver Cafés have per-café and per-board permissions. Member-only boards and closed cafés are not publicly readable, so gated articles are skipped (or kept with public list metadata but no body) and the rest of the run continues. This Actor does not log in and does not attempt to bypass access controls.

Does it fetch images / attachments? Article bodies are returned as text (content) and original HTML (contentHtml, which contains image URLs). Comment stickers are returned as image URLs. Binary files are not downloaded.

Found a bug or need a custom solution? Open a ticket on the Issues tab — feedback and feature requests are welcome.