| Characteristic | Detection | Accuracy |
|---|---|---|
| - | - | - |
| - | - | - |
| - | - | - |
| - | - | - |
| - | - | - |
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SurfShark
May 2026
Worldwide
2025
710 respondents
Online survey
The Bot-detection study was based on 710 participants who played an interactive simulation at Milan Design Week, acting as content moderators to identify bot-generated comments within 120 seconds across four topics.
Share of respondents for the age groups:
Up to 20 years: 8%;
21-30 years: 47%;
31-40 years: 14%;
41-50 years: 7%;
More than 50 years: 7%;
Did not disclose their age: 17%.
Bot-Detection measures the proportion of actual bots a user successfully identified, calculating their ability to prevent bots from slipping by undetected.
Accuracy measures the trustworthiness of a user’s accusations, calculating their ability to avoid falsely flagging real humans.
