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Methodology on the record
Public tiers, calibration tables, and plain-language findings for every fired check — so readers can audit the score, not just trust a logo.
Read methodology ›Press & research
A resource for investigative work
BotShark is built for stories that need reproducible, challengeable evidence — not opaque vendor scores you cannot defend in edit.
Every report says what we looked at, what we couldn't see, and how sure we are.
If you are working on coordination, astroturfing, inauthentic amplification, or reply-farm stories, BotShark gives you a behavioural likelihood score plus the receipts: which rules fired, why they matter in plain language, and which public posts or metrics sit underneath.
We do not hand you a pre-written narrative or a guaranteed “bot” label. We give you explainable analysis you can show to an editor, a lawyer, or a sceptical reader — then you decide what belongs in the piece.
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Public report URLs
Share a finished analysis with editors and fact-checkers before publish. Same /u/handle surface your readers can open later — treat completed scans as public.
Open sample report ›03
Batch a shortlist
Paste every handle in a suspected cluster into a batch, then open reports side by side — not a graph explorer, but fast triage across accounts.
See a sample report ›04
Numbers you can cite carefully
Thresholds are tested against labelled accounts. Use scores as investigative inputs with caveats — never as a courtroom verdict.
Neutrality notes ›1. Start with the accounts that matter to the story — not a random scrape. One handle, a shortlist, or a reply network you already suspect. 2. Read the top findings before the number. If the plain-language signals do not match what you see on the platform, dig or discard. 3. Open the underlying posts when they are linked. Your piece should stand on primary evidence; BotShark is the map, not the destination. 4. Cross-check with your own reporting — timing of a campaign, shared bios, off-platform context, human sources. 5. Cite with method context. Link the report and, where useful, methodology so readers can challenge the work.
What we will and will not do
Will: explain scores, publish methodology, help you interpret a specific report, and talk through higher-volume or academic workflows via contact.
Will not: ghost-write “this person is a bot” copy, promise courtroom certainty, or hand over a private labelled dataset by default. Reports are public by handle; the person scanned can dispute them.
Before you publish
Treat a high score as several independent automation/coordination patterns lined up — not proof of intent, funding, or guilt. Completed scans are public by handle — do not analyse accounts you are not prepared to cite. Empty timelines, polished personas, and weird-but-human accounts all create edge cases. When in doubt, show the evidence and let the reader see the limits.
Field notes on common shapes live on the blog. For a walkthrough of the product surface, see the sample report.