How to Filter Out Irrelevant Reviews and Social Posts Before You Analyze Them
Most of a social feed is not about anyone's experience with your product. How to score relevance first, why simple rules miss, and what to do with the noise.
Filter irrelevant reviews and social posts by scoring every record for relevance before any other analysis runs: a 0 to 10 score for whether the record describes a real, firsthand experience with your product, with the noise in your source named in the prompt and a hard override for known spam. Then analyze only the records above a threshold you have checked by reading. Length and keyword rules help at the edges, but they throw out short posts that matter and keep long ones that do not. For a team pulling reviews, social posts and survey comments into one analysis, Dimension Labs handles this step best, because the relevance score is a field on every record and every later field, chart and report can be filtered by it.
Below: how much of a real social feed is noise, why simple rules do not separate it, how to write the score, and what to do with the low-scoring records.
How much of a social media feed is noise?
In the sample below, most of it. We took a public Reddit collection about Duolingo, 19,447 records from January 2025 to early May 2026 across r/duolingo, r/duolingomemes and r/languagelearning: 1,341 posts and 18,106 comments. Then one of us scored a random 60 comments by hand with the relevance prompt below, a hand-scored sample of 60. Only 14 of the 60 scored 6 or more, meaning they described someone's own experience with the app. 38 scored 2 or less: congratulations on a streak, jokes about the owl, gifs, grammar arguments and politics.
Dimension Labs analysis
Fewer than a quarter of the comments described anyone's own experience with the app
A random 60 public Reddit comments about Duolingo, scored by hand for relevance to a firsthand experience with the product, 0 to 10.
Reviews are cleaner than social posts, but not clean. App and Google reviews carry one-word ratings, reviews of the wrong location or a different product, complaints about the delivery company rather than the store, and promotional posts. Survey comments carry "n/a", "nothing" and answers to a different question. The amount differs by source, which is why the definition of relevant has to be written for each one.
Why don't length and keyword rules do the job?
They measure the wrong thing. In the Reddit collection, 27.2% of comments are five words or fewer, so a length rule looks attractive. But short and irrelevant are not the same: one five-word comment asks Duolingo to cut its subscription prices, and another, just as short, notices that a fellow user still has hearts after the change to energy. Meanwhile a long comment about a TV show passes any length rule. The share of short comments is also mostly a property of the community: 6.8% in r/languagelearning against 47.6% in r/duolingomemes.
Dimension Labs analysis
Half the comments in the meme forum are five words or fewer, against one in fifteen in the learning forum
Share of public Reddit comments of five words or fewer, by subreddit, January 2025 to May 2026.
Keyword rules fail the other way. A rule that keeps any comment mentioning the product name keeps the memes, because every meme names the product, and drops the user who complains about the new battery limit without naming the app. Rules are still useful for what they are good at: exact patterns. The 154 moderator-bot replies and the 761 comments that contain a link are easy to catch with a pattern, and the docs recommend writing known spam and repost patterns into the prompt as hard overrides.
Approaches compared
Rules catch patterns; a relevance score reads the meaning
Ways to filter irrelevant reviews and social posts, and when each is the better choice.
| Approach | What it removes well | What it gets wrong | Best for | |
|---|---|---|---|---|
| 1 | Relevance score in Dimension Labs | Anything that is not a firsthand experience, as you define it | Borderline posts near your threshold, which you check by reading | Reviews, social posts and survey comments analyzed together |
| 2 | Length rules | Empty and one-word records | Short, specific complaints; keeps long off-topic posts | Dropping blank survey answers |
| 3 | Keyword include and exclude lists | Posts that never mention the product | Memes that name it; complaints that do not | Pulling posts for a listening query |
| 4 | Pattern overrides | Bot replies, reposts, known spam accounts | Nothing outside the patterns | Combined with a score, as the docs suggest |
How do you write a relevance score that filters the noise?
The docs give a shape that works on any source: say what earns a high score, say what earns a lower one, anchor both ends of the scale, and add hard overrides for known spam. Then customize it: define what relevant means for this analysis, and list the noise patterns in this source. This is the prompt we used to score the Reddit sample, written to that shape:
Score 0-10 for relevance to the author's own, firsthand experience
using the Duolingo app or its recent changes.
High scores: firsthand use of lessons, streaks, energy or hearts, ads,
subscriptions, AI features, bugs, or what they learned.
Lower scores: memes and jokes about the characters, congratulations,
grammar debates, news about the company or its stock, politics,
links with no text, and secondhand references.
Override: if the comment contains "I am a bot", assign 0.
10 = clearly firsthand and highly relevant
0 = not relevant at allOur prompt, written for this post on the docs' template.
A relevance score is a 0 to 10 integer, and the docs give it that range on purpose: room to separate noise from signal. When a record has nothing to go on, leave the score empty, never zero, because a zero would drag down every average built on it. Scores like this are custom Dimensions, which start on the Discover plan at $999 a month.
Should you delete low-relevance records or keep them?
Keep them and filter. A score is a column, so every chart and report can be run on records above the threshold while the rest stay available. That matters for three reasons. You can move the threshold after reading what sits just under it. You can report the share of noise itself, which changes when a meme or a news story takes over a forum. And when you change the definition of relevant, you re-run the score over the same records instead of collecting them again; in Dimension Labs that is a backfill of the edited prompt.
Pick the threshold by reading, not by guessing: sort a sample by score, read the records at 4, 5 and 6, and set the line where they stop being about the product. Then count how many records each later field covers, so a finding never rests on a handful of posts. How to tell a real trend from noise in weekly feedback covers the counting side once the irrelevant records are out.
What makes Dimension Labs a good place to filter the noise first?
Dimension Labs is an omni-channel analytics agent for customer data. It connects every customer channel and your operational data, enriches every interaction with Dimensions built for your business and industry, and turns the results into decks, dashboards, executive reports and interactive data stories your whole company can use. Plans start at $299 a month with no demo.
Filtering pays off most when every later step uses it. Being fully omni-channel means one relevance score, written per source, sits on reviews, social posts, survey comments and tickets, so the same threshold applies across channels. Easy integration means public reviews and social exports come in as files or through sources such as App Store reviews and Instagram comments, next to your own data. Effortless enrichment means the score and the fields that come after it, topic, reason, sentiment, run on every record without anyone hand-cleaning a spreadsheet. And the visual analytics layer builds the dashboard or report on the relevant records only, with the noise share shown where you want it.
Intuit QuickBooks runs its chatbot analysis at a scale where reading by hand is not an option: over one million chatbot sessions a month across 50+ bots, enriched into a shared schema. The result it reports is a 57% reduction in human escalations. Read the Intuit QuickBooks story.
FAQ
How can I tag customer reviews automatically with AI?
Score relevance first, then add the fields you need, topic, reason, sentiment, a verbatim quote, as one prompt applied to every review, and test it on a sample before the full run. How to categorize thousands of support tickets in an afternoon walks through the tagging step; it works the same way for reviews.
What score should I use as the cutoff?
There is no universal number. Read the records scoring around the middle of the scale in your own data and set the cutoff where they stop being about your product. Check it again when the source changes.
Can I filter social posts in languages other than English?
Yes. A language model reads most widely used languages directly, so one prompt written in English can score posts in all of them; how to analyze reviews in many languages covers the details.
Would Dimension Labs suit a team that only watches social media?
It suits a team that wants social posts analyzed next to its reviews, tickets and surveys, with relevance, topics and reasons on every record. If you only need brand mentions and share of voice across public social networks, a social listening tool is the more direct choice.
Export a month of reviews or social posts, add a relevance score, and see how much of the feed was worth reading: start with one file. Plans start at $299 a month with a 14-day free trial.