Reddit Scraper — built around what you're trying to do

Four common jobs, four ready-to-run setups. Pick the one that matches your goal, copy the input, adapt the subreddit or keyword. Everything below runs request-only — no login, no API keys — and starts on cheap datacenter proxies, escalating to residential only if Reddit rate-limits the run.

One run can return posts, comments, user profiles, and communities at once; each record is tagged with a dataType so mixed output stays sortable.

Sources (give ≥ 1)                    Output — every row tagged dataType
   ─────────────────                     ──────────────────────────────────
   🔗 startUrls  ─┐                       📄 post      title · body · score · media
   🔎 searches   ─┼─▶ request-only  ─▶    💬 comment   threaded · depth · parentId
   📚 subreddits ─┘   datacenter proxy    🧑 user      karma · followers · verified
                      ↓ escalates to      🏘️ community members · rules · type
                      residential on
                      rate-limit
   add-ons:  🎯 filters · 💬 sentiment · 🖼️ downloadMedia · 🔔 onlyNewerThan

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🔬 Audience research — understand a community

Goal: know who a subreddit is, what they post, and who the active voices are.

Scrape a whole subreddit's recent posts, then profile the people behind them.

{
  "subreddits": ["MachineLearning"],
  "sort": "top",
  "time": "month",
  "maxPostsPerSource": 50,
  "maxItems": 100
}

What comes back: post rows (title, body, flair, score, upvoteRatio, numComments, author, subredditSubscribers) plus a community record for the subreddit — membersCount, onlineUsersCount, subredditType, rules, over18. Drop skipCommunityInfo: true if you only want the posts. When a scrape resolves user profiles, you also get user rows with the full karma breakdown (totalKarma, linkKarma, commentKarma), verified, followersCount, and join date — enough to map who carries the community.

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🔔 Brand monitoring — catch every new mention

Goal: get pinged on fresh mentions of your product, wherever they surface.

Search a phrase across all of Reddit (or lock it to one subreddit), then schedule it and return only what's new since last time.

{
  "searches": ["\"acme analytics\""],
  "sort": "new",
  "strictKeywordMatch": true,
  "onlyNewerThan": 1754524800,
  "maxItems": 100
}

What comes back: matching post rows ordered newest-first. strictKeywordMatch drops loose matches that don't contain every keyword; onlyNewerThan (a Unix timestamp) skips anything at or before your last run, so a scheduled run pushes only the new mentions. Restrict the hunt with searchWithinSubreddit, or widen it to comments and communities with searchComments / searchCommunities / searchUsers.

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💬 Sentiment & opinion mining — read the room at scale

Goal: measure how people feel, not just count that they spoke.

Pull a post plus its comment thread and score every piece of text.

{
  "searches": ["\"stable diffusion\""],
  "searchWithinSubreddit": "MachineLearning",
  "sort": "top",
  "time": "month",
  "scrapeComments": true,
  "maxCommentsPerPost": 25,
  "commentSort": "top",
  "sentiment": true
}

What comes back: post and comment rows, each carrying sentimentScore and sentimentLabel (positive / neutral / negative). Comment threads come back deep — collapsed reply chains expanded — with depth, isSubmitter (OP flag), score, and parentId so you can rebuild the tree. commentSort accepts top · new · controversial · old · qa · confidence.

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🗂️ Dataset building — clean text for AI & ML

Goal: a large, tidy corpus of real post/comment text with metadata.

Mix direct URLs, searches, and whole subreddits in a single run; keep permanent copies of any media.

{
  "startUrls": [
    { "url": "https://www.reddit.com/r/dataisbeautiful/" }
  ],
  "subreddits": ["aww", "EarthPorn"],
  "sort": "top",
  "time": "week",
  "postedAfter": "2026-08-01",
  "downloadMedia": true,
  "maxPostsPerSource": 50
}

What comes back: flat post rows with title, body, bodyHtml, postType, domain, award and flag fields, plus media (hasMedia, imageUrls, videoUrls, isGallery, previewImage). startUrls auto-detects each link type — post, subreddit, user, or search page. downloadMedia / downloadAvatars save files to storage as permanent links, since Reddit's own media URLs rotate. Date-bound the corpus with postedAfter / postedBefore (and commentedAfter / commentedBefore for comments).

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🗂️ Everything the Actor captures

Sentiment fields attach only with sentiment: true; permanent media/avatar links only with downloadMedia / downloadAvatars. The dataset ships with Overview, Posts, Comments, Users, and Communities views so each record type reads cleanly.

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⚙️ Input glossary

Sources — provide at least one
startUrls	Reddit URLs — post, subreddit, user, or search (auto-detected)
searches	keywords/phrases across Reddit (quote for exact phrase)
subreddits	subreddits to scrape in full (name, r/name, or URL)

Shaping the results
searchWithinSubreddit	confine a search to one subreddit
searchPosts · searchComments · searchCommunities · searchUsers	what a search returns
sort	relevance · hot · top · new · rising · comments
time	all · hour · day · week · month · year
scrapeComments · maxCommentsPerPost · commentSort	collect & order comment threads
postedAfter · postedBefore · commentedAfter · commentedBefore	date-range filters
onlyWithFlair · strictKeywordMatch · includeNSFW	flair-only, strict match, NSFW toggle
skipUserPosts · skipCommunityInfo	trim user history / community record

Add-ons & limits
sentiment	add sentimentScore + sentimentLabel to posts & comments
downloadMedia · downloadAvatars	save media/avatars to storage as permanent links
onlyNewerThan	skip items at/before a Unix timestamp (monitoring)
maxItems · maxPostsPerSource	global cap and per-source post cap
dedupe	save each record once per run
proxyConfiguration	datacenter by default; auto-escalates to residential on rate-limit
maxConcurrency · maxRetries · requestTimeoutSecs	throughput vs. thoroughness

A note on empties: link and image posts have no body (their contentUrl points to the linked content), and deleted/removed comment bodies come back blank rather than as [deleted].

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⭐ Running this on real work? A quick review on the Apify Store helps other Reddit researchers find it — and tells me which corner to sharpen next.

Tags: reddit · reddit scraper · social media · comments scraper · subreddit scraper · sentiment analysis · user profiles · trend tracking · brand monitoring