Reddit Content Gaps: Compare Your Ranking Queries with Reddit Topics

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October 20, 2026

Google Search Console tells you what you already rank for. Reddit tells you what people are still asking, arguing about and getting wrong. Compare the two to turn content gaps into opportunities.

I built a Streamlit app on top of a Python pipeline that does exactly that: it compares GSC queries with Reddit discussions to identify topics your website doesn’t yet cover and score their potential as content opportunities for your brand.

The pipeline uses cosine similarity and TypeSafe’s Jev to score relevance, Gemini to name topic clusters, and Google’s Knowledge Graph to validate entities, helping prioritise the Reddit topics your brand should target next.

You can try it now: Reddit vs GSC On-Site Content Opportunities

GitHub repository for reference: https://github.com/simodepth96/Reddit-vs-GSC-On-Site-Content-Opportunities/

Once you’ve uncovered topics that you’re not ranking for, you might be curious to explore your subreddit niche for new content opportunities

Table of Contents

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Why compare Reddit with Search Console?

Search Console is a record of aggregated search demand you have already captured. It can’t show you the questions you never ranked for, or the angles your audience cares about that your pages skip.

Reddit threads are the opposite: unfiltered, long-form and written in the audience’s own words. People describe real problems, compare options and ask follow-up questions that rarely show up as tidy keywords. That makes them a rich source of ideas for content enhancements, new sections on existing pages, FAQs and fresh articles.

Doing this by hand means reading hundreds of comments and trying to remember which topics your site already covers. The app automates the comparison.

Two files go in: your Search Console queries (with clicks) and Reddit comments (with scores). Both are routed through ten checks:

▾ Search Console queries + clicksReddit comments + scores

Clean

Five checks tidy both files before anything is compared.

1

Query contains non-ASCII characters?

Queries are lower-cased first, then checked.

Yes✕Drop

No

2

Duplicate of another query?

Duplicates are merged into one query.

Yes◐Merge, sum clicks

No

3

Query is a typo?

Typos are dropped or corrected.

Yes✕Drop or correct

No

4

Comment deleted or removed?

Brand mentions can optionally be stripped from the rest.

Yes✕Drop

No

5

Only a pleasantry, too short or mostly symbols?

Things like “thanks!” carry no topic.

Yes✕Drop

No

Understand and match

Meaning, not exact words, decides what belongs together.

6

Labelled noise by HDBSCAN?

Both sets are embedded with sentence-transformers, then clustered into topics. Noise belongs to no cluster.

Yes✕Drop

No

7

Reddit topic similar to a query topic?

Topics are matched by similarity. Query topics left without a Reddit match are checked in step 10.

No◐Gap: you don’t rank

Yes

Name and validate

Every topic gets a plain-language name and a checked entity.

8

Entity confirmed by the Knowledge Graph?

Gemini names each cluster and proposes an entity. It is accepted only if the Knowledge Graph Search API returns a matching name.

No–No entity

Yes

Score and find gaps

Matches are scored for your brand, then both sides are checked for gaps.

9

Matched pair scored for your brand

TypeSafe Jev gives every matched pair a content opportunity score.

◐Keep, scored

10

Ranking query with no Reddit conversation?

Your site ranks for it, but the community isn’t talking about it.

Yes◐Gap: nobody discusses

★

Your content gaps

Reddit topics you don’t rank for: candidates for new content and keyword ideas.

Ranking queries nobody discusses: where your site speaks to a different audience than Reddit does.

How the content opportunity score works

A raw similarity number can tell you two topics are related, but not whether your brand should publish on them. So for each matched pair, Jev receives your brand profile (what you sell, who you serve, what’s out of scope), both cluster names, their confirmed Knowledge Graph entities, your clicks and some example text. It answers three yes/no questions as probabilities between 0 and 1:

• Brand fit: is your brand a credible publisher on this topic, and is the topic not on your out-of-scope list?
• Entity match: do the Reddit topic and the search topic concern the same confirmed entity, or closely related ones? A topic with no confirmed entity is treated as unvalidated rather than assumed to match.
• Content value: would new content answer real, unmet community questions and capture search demand you could realistically win?

The content_opportunity_score is the weighted average of the three. The default weights are 0.4 / 0.3 / 0.3, and you can change them in the sidebar. The weights are applied after the model calls, so experimenting with them is free.

The Knowledge Graph step is what keeps the scoring grounded. Instead of asking a model to judge two loosely worded topics, you give it a confirmed entity to validate the match against.

What you get back

The app returns five tabs, all downloadable as one Excel file.

How to use it

1. Prepare your files. Export your non-brand queries and clicks from Search Console, and put the Reddit comments and their scores in an xlsx or csv file. The app lets you pick the columns after upload.
2. Add your keys. You bring your own Gemini, Knowledge Graph Search and TypeSafe API keys. I wrote about how you can get the keys here
3. Describe your brand. Add a name, a specific description (products, destinations, audience, USPs) and any topics that are out of scope. The more specific the profile, the more useful the brand-fit probability.
4. Run the analysis and review the Opportunities tab first.

If you get few matches, lower the match threshold. If the clusters are too noisy, adjust the HDBSCAN settings or turn on noise absorption.

Turning the output into content

• High-scoring opportunities: these are topics you already rank for that the community is also discussing. Look at the representative comment and the example queries, then expand the existing page with a new section, an FAQ or a deeper guide that answers the questions people are actually asking.
• Reddit gaps: topics the community cares about that you don’t rank for. Check whether they have real search demand in your keyword tool before committing, then plan new pages or supporting content.
• Query gaps: queries you rank for that Reddit doesn’t discuss. These aren’t a problem, but they show where your site speaks to a different audience than the community does.

A few words of caution

• Reddit discussion isn’t search demand. A topic being popular on a subreddit doesn’t mean people search for it. Use the score to prioritise, then validate with keyword data and the live SERP.
• The score is a model judgement. It’s calibrated and consistent, but it is an input to your decision, not a forecast of traffic.
• Some clusters have no Knowledge Graph entity. Generic topics often don’t, and those pairs score lower on entity match by design.
• Respect the source. Collect Reddit data in line with Reddit’s terms and any rules of the communities you analyse.

Simone De Palma

Technical SEO Manager
Simone De Palma is an SEO Manager at TUI and the founder of SEO Depths.

He graduated in Marketing and Management from Università IULM before completing a degree in Digital Marketing and Data Science at Leeds Beckett University.
Simone has worked as an SEO Specialist in digital agencies in Italy and the United Kingdom and he’s a contributor for the Search Engine Land and Majestic SEO podcast

When he’s away from his double screens, he enjoys cooling down with a refreshing swim at the pool. You could find him exploring art museums or enjoying the company of a classic romance

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