The Design of a Framework for the Detection of Web-Based Dark Patterns

In the theories of User Interfaces (UI) and User Experience (UX), the goal is generally to help understand the needs of users and how software can be best configured to optimize how the users can interact with it by removing any unnecessary barriers. However, some systems are designed to make people unwillingly agree to share more data than they intend to, or to spend more money than they plan to, using 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. Dark Patterns are varied in their form and what they do, and the goal of this research is 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 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: (1) detected in an automated way (either partially or fully), (2) detected in a manual way (either 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

Does Online Political Participation Reinforce Offline Political Participation?: Using Instrumental Variable

The purpose of this study is to investigate whether online political participation can predict the strengthening of offline political participation by using privacy concerns as an instrumental variable. Accordingly, the 2SLS analysis was applied using the Korea Media Panel Survey data of 2016 conducted by the Korea Information Society Development Institute (KISDI). As a result, age and ideological inclination were found to be more important factors in offline political participation than by socioeconomic status. In addition, the use of an instrumental variable to control the direction of causality indicates that online political participation reinforces offline political participation. As a result of habituated daily online activities, it is suggested that a new participatory group, especially low socioeconomic strata, may be mobilized due to the influence of online political participation. This research eliminating the possibility of two-way causality between online and offline political participation is meaningful in finding that online participation activities can reinforce offline political participation and that it is possible to mobilize the groups that were alienated from offline political participation

Assessing the perceived effect of non-pharmaceutical interventions on SARS-Cov-2 transmission risk: an experimental study in Europe

We conduct a large (N = 6567) online experiment to measure the features of non-pharmaceutical interventions (NPIs) that citizens of six European countries perceive to lower the risk of transmission of SARS-Cov-2 the most. We collected data in Bulgaria (n = 1069), France (n = 1108), Poland (n = 1104), Italy (n = 1087), Spain (n = 1102) and Sweden (n = 1097). Based on the features of the most widely adopted public health guidelines to reduce SARS-Cov-2 transmission (mask wearing vs not, outdoor vs indoor contact, short vs 90 min meetings, few vs many people present, and physical distancing of 1 or 2 m), we conducted a discrete choice experiment (DCE) to estimate the public’s perceived risk of SARS-CoV-2 transmission in scenarios that presented mutually exclusive constellations of these features. Our findings indicate that participants’ perception of transmission risk was most influenced by the NPI attributes of mask-wearing and outdoor meetings and the least by NPI attributes that focus on physical distancing, meeting duration, and meeting size. Differentiating by country, gender, age, cognitive style (reflective or intuitive), and perceived freight of COVID-19 moreover allowed us to identify important differences between subgroups. Our findings highlight the importance of improving health policy communication and citizens’ health literacy about the design of NPIs and the transmission risk of SARS-Cov-2 and potentially future viruses

A Framework of Web-Based Dark Patterns that can be Detected Manually or Automatically

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

Understanding Ecommerce Consumer Privacy From the Behavioral Marketers\u27 Viewpoint

Ecommerce sales were expected to increase to $4.8 trillion dollars in 2021 for online retailers in the United States. Behavioral marketers increase sales and revenue by targeting potential customers based on the use of ecommerce consumers\u27 personal information. This correlational research study was framed with the theory of planned behavior. The participants were behavioral marketers based in the United States who completed an online survey. The data were analyzed using multiple regressions and analysis of variance analyses to answer the research question. The results of the analysis answered the research question regarding the correlation between behavioral marketer\u27s attitudes, social norms, and perceived behavioral control (PBC), especially concerning the collection of ecommerce consumers\u27 personal information. The results of the analyses indicated attitude is a strong predictor for behavior intention, as indicated by a positive correlation. The ρ value was greater than .05; therefore, the null hypothesis was rejected. The social norms and PBC variables were not significant. Social norms resulted in F (14,18) = 2.298, ρ = .026. The p value is less than .05; therefore, the null hypothesis was accepted. PBC results were F (78,5) = 4.263, ρ = .048. The p value was less than .05; therefore, the null hypothesis was accepted. The findings showed that behavioral marketers have a strong correlation between their attitude and intention to protect ecommerce privacy. Behavioral managers might benefit from this study and contribute to social change by taking the lead in their organizations to change data collection methods to reduce the number of security breaches

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Altersgerechte Assistenzsysteme leisten einen wichtigen Beitrag, die Lebensumgebung mit intelligenter Technik gesundheitsfördernder und selbstbestimmt zu gestalten. Diverse Sensoren, Geräte und Dienste (z.B. Sturzmeldesysteme, Exergames und Fitnessprogramme) sind heute verfügbar, jedoch steckt die Überprüfung der Wirksamkeit dieser Applikationen noch in den Kinderschuhen. Schlagworte wie „user-centered design“ hin zu einem „partizipatorischen Design“ beschreiben gerade den Umbruch, der in der App-Entwicklung vonstattengeht. Der vorliegende Open Access Sammelband enthält Ergebnisse aus empirischen Studien; aus der Sicht unterschiedlicher wissenschaftlicher Disziplinen wird das Thema erörtert. Dies ist ein Open-Access-Buch. Dies ist ein Open-Access-Buch