In theory, consent dialogs allow users to express privacy preferences regarding how a website and its partners process the user’s personal data. In reality, dialogs often employ subtle design techniques known as dark patterns that nudge users towards accepting more data processing than the user would otherwise accept. Dark patterns undermine user autonomy and can violate privacy laws. We build a system, DarkDialogs, that automatically extracts arbitrary consent dialogs from a website and detects the presence of 10 dark patterns. Evaluating DarkDialogs against a hand-labelled dataset reveals it extracts dialogs with an accuracy of 98.7% and correctly classifies 99% of the studied dark patterns. We deployed DarkDialogs on a sample of 10,992 websites, where it successfully collected 2,417 consent dialogs and found 3,744 different dark patterns automatically present on the consent dialogs. We then test whether dark pattern prevalence is associated with each of: the website’s popularity, the presence of a third-party consent management provider, and the number of ID-like cookies.
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DarkDialogs: Automated detection of 10 dark patterns on cookie dialogs
This research introduces DarkDialogs, a system designed to automatically extract consent dialogs from websites and identify 10 types of dark patterns. Evaluated against a hand-labelled dataset, DarkDialogs boasts a 98.7% accuracy in dialog extraction and a 99% accuracy in classifying dark patterns. When deployed on 10,992 websites, the system successfully collected 2,417 consent dialogs and detected 3,744 distinct dark patterns. The study also investigates the correlation between dark pattern prevalence and website popularity, the use of third-party consent management providers, and the number of ID-like cookies.