A Framework of Web-Based Dark Patterns that can be Detected Manually or Automatically (original) (raw)
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Designing Interactive Systems Conference 2021
Online services pervasively employ manipulative designs (i.e., dark patterns) to influence users to purchase goods and subscriptions, spend more time on-site, or mindlessly accept the harvesting of their personal data. To protect users from the lure of such designs, we asked: are users aware of the presence of dark patterns? If so, are they able to resist them? By surveying 406 individuals, we found that they are generally aware of the influence that manipulative designs can exert on their online behaviour. However, being aware does not equip users with the ability to oppose such influence. We further find that respondents, especially younger ones, often recognise the "darkness" of certain designs, but remain unsure of the actual harm they may suffer. Finally, we discuss a set of interventions (e.g., bright patterns, design frictions, training games, applications to expedite legal enforcement) in the light of our findings. CCS CONCEPTS • Security and privacy → Social aspects of security and privacy; Usability in security and privacy; • Human-centered computing → Empirical studies in HCI; Graphical user interfaces.
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dark patterns in the interfaces of users using sites and portals of online trading affect their behavior by companies that own digital resources. The authors propose to implement the detection of dark patterns on sites in user interfaces using cluster analysis algorithms using two methods for clustering many dark patterns in application interfaces: hierarchical and k-means. The complexity of the implementation lies in the lack of datasets that formalize dark patterns in user interfaces. The authors conducted a study and identified signs of dark patterns based on the use of Nelsen’s antisymmetric principles. The article proposes a technique for assessing dark patterns using linguistic variables and their further interval numerical assessment for implementing cluster data analysis. The last part of the article contains an analysis of two clustering algorithms and an analysis of the methods and procedures for applying them to clustering data according to previously selected features in...
Evil design in the Dark Patterns tunnel: where we came from and where we are (heading) now
2023
In this paper we highlight certain notions on Dark Patterns from a user's perspective. Dark Patterns are elements in interfaces designed to misdirect, confuse, and lure users into unintended, involuntary actions. They are omnipresent in web and game-interfaces and highly effective. There is agreement that awareness and better understanding is needed. We present some current Dark Patterns research projects carried out by the Human-Centered Computing group at Utrecht University, dedicating attention to a set of insights and findings we think should be shared. These stem from research on contextual issue of device choice and experiencing Dark Patterns, the "Dark Pattern Darkness Score" (DPDS), the "System darkness scale" (SDS), and the use of Muscle Memory as a Dark Pattern. The latter we propose as an addition to existing Dark Patters taxonomies.
Dark Patterns and the Emerging Threats of Deceptive Design Practices
Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems
Growth hacking, particularly within the spectre of surveillance capitalism, has led to the widespread use of deceptive, manipulative, and coercive design techniques in the last decade. These challenges exist at the intersection of many diferent technology professions that are rapidly evolving and "shapeshifting" their design practices to confront emerging regulation. A wide range of scholars have increasingly addressed these challenges through the label "dark patterns, " describing the content of deceptive and coercive design practices, the ubiquity of these patterns in contemporary digital systems, and the impact of emerging regulatory and legislative action on the presence of dark patterns. Building on this convergent and trans-disciplinary research area, the aims of this SIG are to: 1) Provide an opportunity for researchers and practitioners to address methodologies for detecting, characterizing, and regulating dark patterns; 2) Identify opportunities for additional empirical work to characterize and demonstrate harms related to dark patterns; and 3) Aid in convergence among HCI, design, computational, regulatory, and legal perspectives on dark patterns. These goals will enable an internationally-diverse, engaged, and impactful research community to address the threats of dark patterns on digital systems. CCS CONCEPTS • Social and professional topics → Computing education; Codes of ethics; • Human-centered computing → Human computer interaction (HCI); Empirical studies in HCI.
Uncovering Dark Patterns in Persuasive Technology
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Dark patterns are interactive design patterns that influence technology users through deception or trickery, and which represent unethical applications of persuasive technology. However, our ability to identify dark patterns is limited , creating a situation where it is difficult to manage abuses of persuasive psychology , because it is difficult to even identify them. Although there are numerous practitioner taxonomies of dark patterns, there is no scientifically-based tax-onomy available. This workshop provides an introduction to dark patterns and an overview of the psychological mechanisms that drive them. Through participa-tory exercises, participants will help to identify the theoretical underpinnings that drive dark patterns, and contribute to the development of a taxonomy of dark patterns, based on consensus within the scientific community. In the workshops, we will form working teams who will review the dark pattern taxonomy, looking for alternative theoretical explanations. Each working team will participate in a group sorting exercise, designed to inform the development of a theoretically-framed taxonomy of dark patterns. All outputs of the workshop will be captured, and used to advance this study towards validation of the taxonomy. After the workshops, the authors of this paper will incorporate all the advancements into the next stage of the research, which will feed into a subsequent paper on a tax-onomy of dark patterns, addressing the identified research questions.
Shedding light on assessing Dark Patterns: Introducing the System Darkness Scale (SDS)
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Dark Patterns are elements in interfaces designed to misdirect, confuse, and lure users into unintended, involuntary actions. These are not just "sloppy" or "inelegant" designs without ill intent but are rather carefully crafted with an understanding of human psychology. Dark patterns are omnipresent as part of web and game-interfaces and highly effective. Hence, there is agreement that awareness and a better understanding is needed. The current study focuses on dark patterns from a user's perspective in order to develop the 'System Darkness Scale' (SDS). The SDS is a set of questionnaire items which can be used to evaluate the darkness of a system and assign a score to it. Just as the SUS proved to be a quick and reliable tool to measure usability, the SDS aims to act as a validated tool to identify in how far a system or service has incorporated "dark mechanisms".
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The popularity and monetary success of casual games are owed to many factors, but some of them that are not entirely honest and ethical. One of them comes in the form of the implementation of a dark pattern, a design pattern that negatively affects the experience of playing the game they are implemented. This research unveils the dark patterns most commonly used in popular and profitable casual mobile games, using heuristic evaluation conducted by five undergraduate student evaluators that have sufficient domain knowledge and experience of the topic at hand. Three of those dark patterns are (1) Pay to Skip:When the game sells various game elements that allow the players to skip some of its core challenges, (2) Grinding: When the game forces the players to sit through repetitive mechanics to make progress in the game, and (3) Playing by Appointment; When the game forces the player to play it during a specific time,through the use of rewards or punishments. The findings of this resear...
Mining the Dark Web: A Novel Approach for Placing a Dark Website under Investigation
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In the last two decades, illicit activities have dramatically increased on the Dark Web. Every year, Dark Web witnesses establishing new markets, in which administrators, vendors, and consumers aim to illegal acquisition and consumption. On the other hand, this rapid growth makes it quite difficult for law and security agencies to detect and investigate all those activities with manual analyses. In this paper, we introduce our approach of utilizing data mining techniques to produce useful patterns from a dark web market contents. We start from a brief description of the methodology on which the research stands, then we present the system modules that perform three basic missions: crawling and extracting the entire market data, data pre-processing, and data mining. The data mining methods include generating Association Rules from products' titles, and from the generated rules, we infer conceptual compositions vendors use when promoting their products. Clustering is the second mining aspect, where the system clusters vendors and products. From the generated clusters, we discuss the common characteristics among clustered objects, find the Top Vendors, and analyze products promoted by the latter, in addition to the most viewed and sold items on the market. Overall, this approach helps in placing a dark website under investigation.
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Social networking sites have increased in popularity and are utilized for many purposes which include connecting with other people, sharing information and creating content. Many people on social networking sites use these platforms to express opinions relating to current affairs within society. People do not realize the value of their data divulged on these platforms and the tactics implemented by social engineers to harvest the seemingly worthless data. An attack vector is created when a user can be profiled using responses from one of these platforms and the data combined with leaked information from another platform. This paper discusses methods for how this data, with no significant value to the users, can become a commodity to social engineers. This paper addresses what information can be deducted from responses on social news sites, as well as investigating how this information can be useful to social engineers.