Reddit Keyword + Comment Thread Monitor

Monitor Reddit for fresh posts and comments that match your keywords across any set of subreddits. Built for recurring research, brand-mention tracking, and lead-signal discovery — point it at the communities you care about, give it a list of terms, and get a clean, structured feed of every match.

What it does

• Scans one or more subreddits for posts (sorted by new, hot, or top).
• Reads the comment threads under matching posts and surfaces comments that hit your keywords too — not just the top-level posts.
• Matches keywords case-insensitively with whole-word boundaries, so short terms like AI, Go, or R match only as standalone words and never inside unrelated text such as "again" or "goes".
• De-duplicates results within a run, so you never get the same post or comment twice.
• Optionally keeps or skips deleted/removed content.

Run it on a schedule (e.g. every hour) to keep an always-fresh dataset of mentions, or run it ad hoc to research a topic.

Input

Field	Type	Description
subreddits	array of strings	Required. Subreddits to monitor, without the r/ prefix (e.g. python, marketing).
keywords	array of strings	Keywords or phrases to match in titles, post text, and comments. Leave empty to capture every post and comment.
sort	string	Post ordering per subreddit: new, hot, or top. Default new.
timeRange	string	Time window when sort is top: day, week, month, year, all. Default week.
maxPosts	integer	Max matching posts to collect per subreddit (1–200). Default 25.
maxCommentsPerPost	integer	Max matching comments per post (0–500). Set 0 to track posts only. Default 20.
includeDeleted	boolean	Keep deleted/removed posts and comments. Default false.
proxyConfiguration	object	Optional proxy configuration. Recommended for large or frequent runs to keep results reliable.

Example input

{
  "subreddits": ["MachineLearning", "technology"],
  "keywords": ["ai", "llm", "open source"],
  "sort": "new",
  "maxPosts": 25,
  "maxCommentsPerPost": 20
}

Output

Every matched post and comment is pushed to the dataset as a flat record:

{
  "recordType": "comment",
  "postId": "1abc234",
  "commentId": "kx9y8z7",
  "subreddit": "MachineLearning",
  "author": "some_user",
  "title": null,
  "body": "This new open source LLM is impressive…",
  "score": 42,
  "createdUtc": 1716800000,
  "permalink": "https://www.reddit.com/r/MachineLearning/comments/1abc234/_/kx9y8z7/",
  "matchedKeywords": ["open source", "llm"]
}

Field	Description
recordType	post or comment.
postId	Reddit ID of the post.
commentId	Reddit ID of the comment (empty for posts).
subreddit	Subreddit the record belongs to.
author	Reddit username of the author.
title	Post title (empty for comments).
body	Post text or comment text.
score	Net upvote score.
createdUtc	Unix timestamp of creation.
permalink	Full Reddit URL to the post or comment.
matchedKeywords	Keywords that matched this record.

Pricing

This actor uses a pay-per-event model:

• Result item produced — charged for each post or comment saved.
• Comment enriched — charged for each comment record enriched with its thread context.

You only pay for the matches you actually receive.

Tips

• Start with a low maxPosts / maxCommentsPerPost to gauge volume, then scale up.
• Use sort: top with a timeRange for digests; use sort: new for live monitoring.
• Leave keywords empty to ingest an entire subreddit feed.
• For large or frequent runs, enable proxy configuration for more reliable results.