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Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence


Computer Sciences


This research explores the design and development of a framework for the detection of Dark Patterns, which are a series of user interface tricks that manipulate users into actions that they do not intend to do, for example, share more data than they want to, or spend more money than they plan to. The interface does this using either deception or other psychological nudges. User Interface experts have categorized a number of these tricks that are commonly used and have called them Dark Patterns. They are typically varied in their form and what they do, and the goal of this research is to explore existing research into these patterns, and to design and develop a framework for automated detection of potential instances of web-based dark patterns. To achieve this, we explore each of the many canonical dark patterns and identify whether or not it is technically possible to automatically detect that particular pattern. Some patterns are easier to detect than others, and there are others that are impossible to detect in an automated fashion. For example, some patterns are straightforward and use confusing terminology to flummox the users, e.g. “Click here if you do not wish to opt out of our mailing list”, and these are reasonably simple to detect, whereas others, for example, sites that prevent users from doing a price comparison with similar products might not be readily detectable. This paper presents a framework to automatically detect dark patterns. We present and analyze known dark patterns in terms of whether they can be either: (1) detected in an automated way (it can be partially or fully), (2) detected in a manual way (it can be partially or fully) and (3) cannot be detected at all. We present the results of our analysis and outline a proposed software tool to detect dark patterns on websites, social media platforms and mobile applications.