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 methodologyPress & 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.
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.
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 reportBatch 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 how it worksNumbers you can cite carefully
Thresholds are tested against labelled accounts. Use scores as investigative inputs with caveats — never as a courtroom verdict.
Neutrality notes1. 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, hand over a private labelled dataset by default, or run a public accusation database.
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 how it works.