LessBother.

REVIEW TRUST · LOCAL ANALYSIS LAB

Are the reviews behaving like real independent opinions?

LessBother looks for manipulation patterns—bursts, duplicated language, reviewer reuse and suspicious rating shapes—then shows the evidence. It does not stamp individual people “bot” from a hunch.

PRIVATE BY DEFAULT

Analyze a review dataset

Pasted review datasets never leave this page. If the review-data box is empty, enter up to three product URLs. LessBother makes a separate bounded server-side fetch of each public page and combines only product-scoped structured review records locally. Comparing listings can expose review copy that travels between products or storefronts.

Useful fields: text, rating, timestamp/date, author/reviewer, verified. More metadata means stronger conclusions. URL collection does not store the submitted URLs or fetched pages; each retailer page may still expose only a partial review sample. Up to three sources can be compared in one local analysis.

Accepted data format
[
  {"author":"buyer17", "rating":2,
   "timestamp":"2026-08-12T14:30:00Z",
   "text":"Zipper split after three uses."}
]

Tab-separated exports are also accepted. If you paste full product-page source, LessBother reads only product-scoped schema.org Review data and refuses ambiguous multi-product structured data.

Detect coordination, not souls

A bot detector becomes unreliable when it pretends one weird sentence proves an account is fake. LessBother combines independent signals and reports uncertainty.

Show the evidence

A trust label always comes with the underlying counts and patterns. No unexplained “82% authentic” magic number.

Same engine, bigger target

The same Human Trust Engine also has a working Conversation Authenticity lab for Reddit-style reply clusters and manufactured consensus.

Analyze a conversation →