As digital interfaces become increasingly prevalent, certain manipulative design elements have emerged that may harm user interests, raising associated ethical concerns and bringing dark patterns into focus as a significant research topic. Manipulative design strategies are widely used in user interfaces (UI) primarily to guide user behavior in ways that favor service providers, often at the cost of the users themselves. This paper addresses three main challenges in dark pattern research: inconsistencies and incompleteness in classification, limitations of detection tools, and insufficient comprehensiveness in existing datasets. In this study, we propose a comprehensive analytical framework--the Dark Pattern Analysis Framework (DPAF). Using this framework, we developed a taxonomy comprising 68 types of dark patterns, each annotated in detail to illustrate its impact on users, potential scenarios, and real-world examples, validated through industry surveys. Furthermore, we evaluated the effectiveness of current detection tools and assessed the completeness of available datasets. Our findings indicate that, among the 8 detection tools studied, only 31 types of dark patterns are identifiable, resulting in a coverage rate of just 45.5%. Similarly, our analysis of four datasets, encompassing 5,561 instances, reveals coverage of only 30 types of dark patterns, with an overall coverage rate of 44%. Based on the available datasets, we standardized classifications and merged datasets to form a unified image dataset and a unified text dataset. These results highlight significant room for improvement in the field of dark pattern detection. This research not only deepens our understanding of dark pattern classification and detection tools but also offers valuable insights for future research and practice in this domain.

翻译：随着数字界面日益普及，某些可能损害用户利益的操纵性设计元素逐渐浮现，引发了相关的伦理关切，并使暗黑模式成为一个重要的研究课题。操纵性设计策略在用户界面（UI）中被广泛使用，主要是为了引导用户行为以利于服务提供商，而这往往以用户自身利益为代价。本文针对暗黑模式研究中的三个主要挑战展开探讨：分类的不一致性与不完整性、检测工具的局限性以及现有数据集的不够全面。在本研究中，我们提出了一个综合性的分析框架——暗黑模式分析框架（DPAF）。利用该框架，我们构建了一个包含68种暗黑模式的分类体系，每种模式均通过行业调查验证，并详细标注了其对用户的影响、潜在场景及现实案例。此外，我们评估了现有检测工具的有效性，并对可用数据集的完整性进行了分析。我们的研究结果表明，在所研究的8种检测工具中，仅能识别出31种暗黑模式，覆盖率仅为45.5%。同样，我们对四个数据集（共包含5,561个实例）的分析显示，仅覆盖了30种暗黑模式，总体覆盖率为44%。基于现有数据集，我们进行了分类标准化处理，并将数据集合并，形成了一个统一的图像数据集和一个统一的文本数据集。这些结果凸显了暗黑模式检测领域存在显著的改进空间。本研究不仅深化了我们对暗黑模式分类及检测工具的理解，也为该领域未来的研究与实践提供了有价值的见解。