How to Track Reddit Keywords With Less Noise

Collection setup

Open the Reddit Search Scraper actor and the Reddit Brand Mention Snapshot task. The tool inputs are query, optional subreddits, maxResults, sort, and time. The current preset query uses `"Brand Name"`, maxResults 100, sort new, and time month. Replace that query with your own relevant phrase and start with maxResults 3. MaxResults is a shared cap, not a per-subreddit quota.

Each dataset row retains exact fields: inputQuery, requestedSubreddit, matchedSubreddit, sort, time, rank, postId, canonicalPostUrl, title, body, createdAt, status, statusReason, unavailableFields, and observedAt. Source rank shows order, not relevance. A missing body or context stays uncertain.

The checked three-row learnpython control reached its cap. This does not predict every reader run reaches its cap or validate reader query relevance. Global isComplete equals true means a processing outcome, not a full source population. A row requestedSubreddit or matchedSubreddit can differ from the summary subreddit.

Output searchResults entries include query, subreddit, status, reason, candidatesFound, rowsSaved, duplicatesSkipped, endReason, source, and sourceUrl. The run summary includes status, statusReason, isComplete, query, searchesPlanned, searchesSucceeded, candidatesFound, usefulRows, rowsSaved, billableRows, freeRows, duplicatesSkipped, stopReason, and observedAt. The control searchResults[0] had subreddit learnpython, status ok, rowsSaved 3, endReason max_results_reached, and source reddit_rss.

Complete Research Method

1. Define your exact research problem and inclusion rule before searching. Name your intended product or topic and context. Words that name sports or animals need clear context.
2. Save an initial plain or quoted query plus sort, time window, and cap. Use one small batch so you can inspect every source post. Keep unchanged rows and input and output intact.
3. Read the title, body, and source discussion where necessary. Label each result as relevant, irrelevant, or uncertain with a clear reason. Do not assume a body mentioning your brand asks about its product. Do not code unavailable text as irrelevant.
4. List why false matches occurred. Check for ambiguous names, supplier promotions, job listings, unrelated uses, wrong questions, or unavailable context. These reasons guide a better query.
5. Revise one factor at a time. Add a problem term, use a clearer phrase, or restrict your search to a justified community. Keep your old and new scopes for comparison.
6. Repeat a small test with the same review rule. Report relevant counts and uncertain counts separately. Show all collected and uncertain counts beside your working ratio. Small samples are not precise benchmarks.
7. Choose the clearer query only if it still captures your intended problem types. Narrowing too much can hide other useful language. Test a complementary phrase where needed.
8. Save a query test sheet with input, scope, source IDs, labels, reasons, and your selected next query. Routine collection and scheduling happen later. Keep changes visible.

Fictional Test Example

All query results and product names are invented. Product Orbit is a project tool. A broad query for Orbit returns 6 posts. Two are about project setup, two are about astronomy, one is a supplier promotion with no user problem, and one has an unavailable body. The labels are 2 relevant, 3 irrelevant, and 1 uncertain. Across all collected posts, 2 out of 6 or 33.3 percent are relevant. Across the 5 resolved posts, 2 out of 5 or 40 percent are relevant. Neither number is a real tool accuracy claim.

A revised phrase for project setup yields 4 posts. There are 3 relevant setup discussions and 1 uncertain inaccessible body. The all collected relevant share is 3 out of 4 or 75 percent. The resolved share is 3 out of 3 or 100 percent, with 1 unresolved shown. A complementary migration query batch is suggested to check migration problems that the setup phrase misses.

The query test sheet below lists 10 fictional query appearances and their labels. One source ID appears in both batches, keeping 2 query appearances but 1 source identity in a combined research set.

Decisions select the setup phrase for setup questions, retain migration as a needed scope test, and leave unavailable bodies uncertain. No reply or sentiment output follows.