Sample report
See the full analysis surface your participants or reviewers would open.
Open sampleAcademia & labs
Exportable reports, explainable findings, and public calibration you can cite — built for platform-integrity and computational social science work.
BotShark is a transparent heuristic stack, not a closed classifier. That matters when a paper, dissertation, or lab note needs to say exactly what was measured.
Rule-level explainability — which signals fired, in plain language, with linked public evidence where available
Exports — JSON and PDF of a report you ran, plus a public `/u/handle` URL for supplements
Calibration tables — precision/recall style checks on labelled cohorts (methodology)
Batch + library workflows — paste seed sets, store handle lists, and re-open scored accounts for an event window
Event studies around elections, product launches, or harassment campaigns
Comparing scores across organic vs amplified account shortlists
Auditing influencer or advocacy cohorts for automation density
Teaching materials where students must show their work
Public data only. Empty timelines understate recall. Score ≠ intent. There is no downloadable full rule YAML for outsiders — cite the public methodology page and the fired findings on each report. Please say which cohort you used if you quote calibration numbers.
Higher-volume or collaboration conversations start at contact — that is a conversation, not a packaged research dump. Journalists and mixed newsroom–lab teams should also see for journalists.
See the full analysis surface your participants or reviewers would open.
Open sampleCollect → extract → score → explain, in plain language.
How it worksBehaviour over ideology — how we talk about non-partisan sampling.
Neutrality