Activity messages
Randomised recent-purchase notification
A fictional store displays “12 people bought this in the last hour” using a random number unrelated to completed orders.
Individual HTML example · Social proof
Fabricated named signup notification
A fictional course page rotates messages such as “Mia from Berlin just enrolled” using invented names and locations rather than completed enrolments.
This is an original fictional comparison. It depicts no real company and is not a legal finding or safe harbour.
Fabricated named signup notification
A fictional course page rotates messages such as “Mia from Berlin just enrolled” using invented names and locations rather than completed enrolments.
New learner joined. Invented names and cities are presented as completed enrolment events.. Mia from Berlin enrolled. Claim basis, collapsed
Course participation. A verified aggregate states its period without fabricating an individual event.. 124 enrolments this month. Claim basis, expanded: The relevant population, period, inventory or offer state is presented with the message.. View the source and period for “124 enrolments this month”
Why the first version can mislead: The interface asks the user to rely on other people’s behaviour or approval. The cue “Mia from Berlin enrolled” has persuasive meaning only if its population, period, metric and relationship match the represented claim. The panel instead says: “Invented names and cities are presented as completed enrolment events.” A visitor may infer genuine recent participation and social confidence from activity that never occurred.
What a fairer design does: Show only verified activity with a defined period and privacy-safe aggregation, or remove the notification.
Show annotated differences (2)
Potentially problematic version
- Claim basis, collapsedIn “Fabricated named signup notification”, this element shows how activity messages can shape the decision.
Neutral alternative
- View the source and period for “124 enrolments this month”In “Fabricated named signup notification”, this element keeps the clearer alternative visible at the same decision point.
Review questions (3)
- Which source record substantiates “Mia from Berlin enrolled”, including its population, period, metric and any commercial relationship?
- Record the claim, timestamp, data source and post-claim state; which evidence supports “It appears near a transactional choice or repeatedly interrupts it”?
- Can the claim be reproduced from a defined population and period, or does the evidence instead fit “named testimonial”?
Complete pattern context
How this example relates to Activity messages
Claims about recent or current actions by other users are used to steer the decision, particularly where authenticity, timing or relevance is unsupported. The label describes a recurring design mechanism; whether a particular implementation is harmful or unlawful depends on the complete journey, audience, evidence and rules within scope.
The full guide explains inclusion and exclusion criteria, harms, fair design principles, evidence questions and the relevant EU legal information layers. The example page isolates one scenario so it can be shared and discussed without reproducing the complete library on one page.
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Popularity claims
“Most popular” has no defined metric
A fictional service labels its highest-margin plan “Most popular” without recording sales, selection period or the population being measured.
Popularity claims
Bestseller badge follows margin rather than sales
A fictional marketplace assigns “Bestseller” to whichever item yields the highest commission, regardless of units sold.
Sources and provenance for this example
Evidence base
Sources
- Dark commercial patternsOrganisation for Economic Co-operation and Development · Secondary · checked 2026-09-14 · OECD Digital Economy Papers No. 336
- Unfair Commercial Practices DirectiveEuropean Parliament and Council of the European Union · Primary · checked 2026-08-09 · Directive 2005/29/EC; CELEX 02005L0029-20220528