Defametrics.

Research library

What the economic evidence actually shows

Studies are graded for quality and causal design, and every entry carries its limitations. None of this literature is about defamation, and almost none of it is British — those facts are stated on the face of each record and reduce the weight the calculator gives it.

Transferability warning: consumer-review economics measures the effect of ratings on trade in specific markets. It does not measure the effect of a defamatory allegation on a particular business. Effects reported here are indicative context, never proof of loss in an individual case, and studies reporting small or null effects are retained deliberately.
Studies held
5
Feed the model
0
Context only
5
Government sources
0

Studies

Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud

Quality D

Michael Luca, Georgios Zervas · 2016 · Management Science · Peer reviewed · United States

Roughly 16% of restaurant reviews were filtered as suspicious; fraud rises when reputation is weak or competition intense

Limitations: Establishes prevalence of fake reviews, not the financial loss caused to a specific business.

Causal strength 55/100 · calculator weight 0.5

Context only — no quantified revenue effect

Reviews, Reputation, and Revenue: The Case of Yelp.com

Quality C

Michael Luca · 2016 · Harvard Business School Working Paper · Not peer reviewed · United States

A one-star increase in Yelp rating is associated with roughly a 5–9% increase in revenue for independent restaurants

Limitations: US independent restaurants only; chains show no effect; not directly transferable to UK B2B or professional services.

Causal strength 80/100 · calculator weight 0.8

Context only — below the peer-reviewed quality gate

Promotional Reviews: An Empirical Investigation of Online Review Manipulation

Quality B

Dina Mayzlin, Yaniv Dover, Judith Chevalier · 2014 · American Economic Review · Peer reviewed · United States

Independent hotels with a neighbouring competitor showed significantly more suspicious reviews

Limitations: Concerns manipulation incentives rather than the revenue effect of one defamatory publication.

Causal strength 80/100 · calculator weight 0.55

Context only — no quantified revenue effect

Learning from the Crowd: Regression Discontinuity Estimates of the Effects of an Online Review Database

Quality C

Michael Anderson, Jeremy Magruder · 2012 · The Economic Journal · Peer reviewed · United States

An extra half-star caused restaurants to sell out substantially more frequently

Limitations: Measures booking availability rather than audited revenue; US casual dining setting.

Causal strength 85/100 · calculator weight 0.85

Context only — no quantified revenue effect

The Impact of Online User Reviews on Hotel Room Sales

Quality D

Qiang Ye, Rob Law, Bin Gu · 2009 · International Journal of Hospitality Management · Peer reviewed · China

A 10% improvement in reviewer ratings was associated with roughly a 4.4% increase in bookings

Limitations: Cross-sectional observational design; Chinese online travel market; correlation not established causation.

Causal strength 45/100 · calculator weight 0.45

Context only — below the peer-reviewed quality gate

Government and regulator evidence