Evidence-based attention audits for tokens: who is actually talking, how much of it is manufactured, and whether the crowd survives contact with a spreadsheet. Answer in 72 hours, methodology fully public.
Request an audit See a sample reportCrypto proves almost everything. Proof of work settles who wrote a block, proof of stake settles who gets a vote, and anyone who cares to can check a transaction themselves. The one claim nobody verifies is the one every project leads with, that there are real people here.
Most social spikes are spam-heavy, and manufactured hype measurably underperforms. Absolute spam numbers mislead though, because some legitimate communities live in noisy neighborhoods and some clean-looking spikes are three accounts wearing a crowd costume. Telling them apart takes baselines, composition analysis, and decay signatures, not vibes.
I build and publish that analysis daily. An audit is the professional-depth version, run on the token you care about, with every claim backed by evidence you can check.
All findings are published, era-audited, and reproducible. Every audit shows its work. The research sits in 35 open-source projects, where the null results are written up as carefully as the findings.
Spam share vs the token's own baseline, creator concentration, megaphone vs botnet vs broad-crowd patterns, contributor breadth.
Every attention spike in the token's history, classified. Decay and residue signatures: does attention compound or round-trip?
Fresh-wave timing, account concentration behind spikes, and cross-checks against known manufactured patterns.
Did the conversation produce wallets? First-time holders per million interactions, measured across every deployment, on spike days versus ordinary ones. Two independent measurements pointing the same direction is the strongest evidence this method produces.
Every spike gets a source: broad crowd, coordinated botnet, single megaphone, or institutional broadcast (exchange promos and alert feeds). Identical concentration numbers, completely different meanings.
A written, evidence-first assessment: what the data supports, what it rules out, and what it can't determine. No adjectives without numbers.
You name the token and the question: pre-listing diligence, pre-investment check, or proof for your own community. Fixed price, fixed turnaround, no surprises.
The full pipeline runs: baselines, spike history, composition, creator forensics. The same open-source methodology as the public work, at professional depth.
A written report with every claim tied to evidence, plus a walkthrough call on Full Audits. You get the data files too: check my work.
Tell me the token and the question you need answered. Fixed price, fixed turnaround, and a reply within one business day.
No, and I won't pretend to. The data measures amplification patterns: spam waves, account concentration, timing. Bot campaigns, coordinated advocacy, and algorithmic amplification can produce overlapping signatures. Reports state what the evidence supports and what it can't determine.
Then that's what the report says, and you'll see exactly why: every claim ties to data. Many "bad" results are actually the chronic-baseline case: a noisy neighborhood, not fresh manufacturing, and the report distinguishes those. What I won't do is soften a verdict because the subject is paying.
No, it gets labeled and then separated out. Exchange marketing accounts and automated alert feeds can dominate a token's interaction counts without a single person forming an opinion, so an audit identifies those accounts, reports them as institutional broadcast rather than manufactured hype, and then shows what your organic conversation looks like with that volume removed. Useful in both directions: it explains inflated numbers, and it reveals the real community underneath them.
Social and market data comes from LunarCrush's API, including their post-level spam classification, which my published backtests found carries real signal. I'm a LunarCrush affiliate and disclose that on everything. The analysis, calibration, and conclusions are my own.
Because it has a public track record: daily published verdicts, and research findings I stress-test in public, including the times my own headline claims didn't survive their audits. Every report ties each claim to evidence and includes a reproduction appendix, so you can verify the work you paid for.
No. Audits describe conversation authenticity, not investment merit. A token can have a real community and a bad product, or manufactured hype and excellent technology.
Yes, that's the Monitoring tier: if someone wash-hypes your token to make your community look botted, or a fresh spam wave rides your genuine announcement, you'll know with evidence in hand.
Nicki Sanders: engineering leader and blockchain investigator. I publish daily hype-detection verdicts and stress-test my own findings in public, including the times they didn't survive. The track record is the pitch: follow the daily verdicts on X.