A rival account has 84,000 followers to your 31,000, and every meeting where that number comes up ends the same way.
Then you look at their posts. Twelve likes. Four replies, two of which are from the same account. A retweet count that would embarrass a much smaller profile.
Something in that picture is not adding up, and there are only a few explanations. Their content is genuinely not landing. Their audience is in a different time zone than their posting schedule. Or a meaningful slice of those 84,000 accounts are not people at all.
The follower count is the most quoted number on X and the least informative one. It counts accounts, not audience, and those stop being the same thing the moment inauthentic accounts enter the picture. Working out how many of a competitor's followers are fake turns a number everyone repeats into a number that means something.
With Circleboom Twitter, you can run your account and up to five competitors through one benchmark report and read the human versus fake and bot split for every account side by side.
What Is Circleboom Twitter?
Circleboom is an Official X Enterprise Developer. Every number in the report comes from X's official API, pulled as an official Enterprise Developer, with no scraping and no estimates.
- Compares your account against up to 5 competitors across 51 parameters in 9 sections, all rendered side by side
- Reports the percentage split of human versus suspected fake and bot followers for every account in the set
- Applies a multi signal classification model rather than flagging accounts on any single suspicious trait
- Breaks each audience into high, medium, and low quality tiers alongside active, inactive, overactive, and verified splits
- Pulls any public account's full follower list when you want to inspect the individual accounts rather than the summary
Every run is dated, and re-running the same competitor set produces a new dated result rather than overwriting the old one.
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How to Check How Many of a Competitor's Followers Are Fake
- Connect your X account to Circleboom Twitter and open the X Competitor Benchmark Report. Authentication runs through the official X Enterprise API.
- Enter your own handle first, then add the competitors. There is one input per handle and an "+ Add Competitor X Handle (max 5)" control. Your own account belongs in the set. A competitor's fake percentage means nothing without yours next to it, because some level of inauthentic followers is normal for everybody.
- Choose the comparison set deliberately. Five slots is not many. Pick accounts you actually compete with for the same audience rather than the biggest names in the category, because the useful comparison is with accounts at a similar stage.
- Click Run Comparison. Circleboom retrieves followers, following, and recent tweets for every account in the set through the official API, then renders the same sections for each one in the same order.
- Go to Section 4.2, Audience Quality. This is the section that answers the question. The rest of the report is worth reading, but everything about authenticity lives here.
- Read the % Human versus % Fake/Bot row first. This is the direct answer, given as a split for each account in the set. It is also the truest test of whether the follower count lead in the earlier section is real.
- Read % Active versus % Inactive immediately after. This is calculated from average tweeting frequency rather than last tweet date. Inactive is not the same as fake, and an account can have a clean bot split and still have an audience that is mostly asleep, which produces the same weak engagement from a completely different cause.
- Check % Ordinary versus % Overactive. A high overactive share is not automatically bad, since some of those accounts are genuine power users. Read it next to the fake and bot number, because overactive and fake together is the combination that usually means automation.
- Look at the quality tiers row. High, medium, and low tiers are built from activity, follower to following ratio, and account age. This is the clearest single summary in the section: a large high tier relative to low is the strongest evidence that an audience advantage is real.
- Check the verified split, and read it carefully. Blue, gold, and gray are reported separately for a reason. Blue can be paid, so it is not a credibility guarantee on its own. Gold and gray carry stronger organizational context, but still need reading alongside engagement.
- Do the arithmetic that makes the headline number honest. Take the competitor's follower count and multiply by the human percentage. That is their effective audience, and it is the number worth putting in a deck instead of the raw one.
- Inspect individual accounts if the summary looks extreme. Pull the competitor's follower list directly and sort by follow ratio ascending. If the suspicious accounts are obviously manufactured, the summary is confirmed. If they look like ordinary quiet people, the classification is catching lurkers and you should say so.
- Re-run monthly with the UPDATE control. Past comparisons are saved on the landing page with their run date, and UPDATE re-pulls the same handle set with fresh data. One reading is a snapshot. The trend is what tells you whether a rival's audience quality is improving or whether their growth is just louder follow-hacking.
Why the Fake Percentage Changes the Whole Comparison
Section 4.2 opens with a line worth keeping: not all followers are equal, and the algorithm cares.
X tests a new post against a small slice of the follower list before deciding whether to push it wider. If a large share of that list is inauthentic or dormant, the test group is unreliable and the post underperforms regardless of how good it is. That is why a bloated follower count can actively work against an account rather than simply flattering it.
The same mechanism is why fake followers damage every ratio derived from the follower count. An inauthentic account inflates the denominator of engagement rate without ever contributing to the numerator. A post reaching 10,000 followers where 3,000 are inauthentic has a very different effective reach from one where all 10,000 are real.
📌 A competitor with fewer followers but a cleaner split is winning the audience quality comparison even while losing the header row. That is the finding this exercise exists to produce.
It also reframes results you may have misread. If a rival posts constantly and gets very little back, poor audience quality is a more likely explanation than poor content, and why organic reach has tightened for small and mid sized accounts plus the hidden ranking signals behind distribution fill in the mechanics. What a good like to follower ratio looks like is the quick sanity check you can run on any account before doing anything else.
How the Classification Works, and What It Cannot Tell You
Being precise here matters, because the output is a probability and it is easy to quote as a fact.
The model is composite, not single signal. An account with a low tweet count alone is not flagged. An account with a low tweet count, a follow ratio below 0.05, no profile photo, a very recent creation date, and an inactive engagement classification is. The more signals that align, the more confident the classification. How the fake and bot detection works on your own audience, a bot checker in practice, and checking whether someone's followers are fake or real all describe the same approach applied at different scales.
⚠️ Three things this number does not tell you, and getting these wrong is how a benchmark becomes a bad accusation.
It does not tell you they bought followers. Bot follows are unsolicited. Any account of any size accumulates them, and a viral moment or a spell of visibility in a spam-heavy niche will deliver thousands nobody asked for. A high fake percentage is evidence of an inflated count, not of misconduct. Why X carries so many scam accounts and bots that keep following accounts unprompted explain how ordinary this is.
It does not tell you the accounts are malicious. Automated accounts are not all bots in the pejorative sense, and the difference between bots and legitimate automated accounts is worth understanding before you write anything down.
It is a classification, not a verdict. Treat the percentage as a candidate estimate with a stated method, not as a measured fact. If you are putting it in a document somebody else will read, say how it was derived. Validating followers and analyzing another account's followers both support the check if you want to inspect the underlying accounts yourself.
What to Do With the Answer
Fix your own number first. Running this on a competitor and ignoring your own row is the most common way the exercise gets wasted. Check how many of your own followers are bots, and if the answer is uncomfortable, decide whether to remove or block them and clean out the spam followers.
Re-benchmark with effective audience, not follower count. Recompute every comparison in your reporting using follower count multiplied by the human percentage. Some rivals shrink considerably and some barely move, and the gap between those two groups is the actual competitive picture.
Mine the real portion of their audience. The authentic followers of a competitor are a genuinely good targeting pool, because they have proven topical interest. Targeting a competitor's followers with ads, analyzing someone else's followers, and tools for analyzing another account's network all start here.
Check the overlap. Shared followers tell you how much of the market you already reach and how much is genuinely theirs. Comparing followers between two accounts, finding the intersection, and finding common followers without coding cover the mechanics.
Vet influencers the same way. Before committing budget, run the candidates as the comparison set and read audience quality, engagement rate, and posting consistency side by side rather than choosing on follower count. Finding influencers for your brand and finding X influencers step by step cover discovery; this covers the due diligence.
Fold it into the wider audit. Analyzing a competitor's X account, competitor analysis on X, and the metrics worth tracking put audience quality in context with everything else, and improving your own engagement rates is where the work actually goes.
FAQ
Can you check the fake follower percentage of an account you do not own?
Yes, for any public account. The benchmark report takes your handle plus up to five competitor handles and reports the same audience quality breakdown for every account in the set.
What counts as a normal fake follower percentage?
There is no universal threshold, which is exactly why your own account belongs in the comparison. Every account accumulates unsolicited bot follows. What matters is the gap between accounts in the same category, not the absolute figure.
How is a fake follower identified?
Through a composite model using activity level, follower to following ratio, account age, tweet frequency, and profile completeness. No single signal flags an account. The classification requires several to align.
Does a high fake percentage mean they bought followers?
No, and this is the most important caveat in the topic. Bot follows arrive unsolicited, and visibility attracts them. The number tells you the count is inflated, not how it got that way.
Can I see the actual accounts, not just the percentage?
Yes. Pull the account's follower list directly and sort or filter it, which lets you verify the classification against real profiles rather than trusting a summary.
How many competitors can I compare at once?
Up to five, plus your own account, in a single report. All accounts render side by side in every section.
How often should I run it?
Monthly is the practical floor. Audience quality, posting rhythm, and engagement rate move over weeks rather than days, so a monthly re-run catches real shifts without generating noise. Each run is dated and saved, and the UPDATE control re-pulls the same set.
Should I publish a competitor's fake follower percentage?
Be careful. It is a classification with a method, not a measured fact, and presenting it as an accusation is both unfair and easy to challenge. If you cite it, state how it was derived.
Final Thoughts
The follower count is the number everyone quotes and the one that survives the least scrutiny. It counts accounts. Whether those accounts are people is a separate question that nobody asks out loud.
Working out how many of a competitor's followers are fake is not really about them. It is about replacing a comparison built on a headline number with one built on effective audience, which usually reorders the field and occasionally reverses it.
With Circleboom Twitter, you can benchmark your X account against up to five competitors, read the human versus fake and bot split for each of them side by side, and find out whether the account you have been measuring yourself against is actually ahead.
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