Abstract
Deceptive Design, or “Dark Patterns” in UI/UX are becoming increasingly prevalent and sophisticated in nature. As a result, users frequently encounter them in contexts such as e-commerce and social media whether it’s unbeknownst to the user or not. While previous research has explored user perception and attitudes towards “Dark Patterns”, little research has been conducted to investigate its potential impacts on a user’s mental health. The purpose of this paper is to identify vulnerable demographics of users who may face harmful outcomes as a result of interacting with deceptive design. From this, we generate a set of research questions that are intended to generate discourse and further investigation into the possible impacts of Dark Patterns on these users.
Introduction
The growth of ecommerce, social media, and the internet has led companies to constantly seek out new means of growing user bases and increase revenue streams. One such method of attaining these goals has been through the implementation of “deceptive design” or “Dark Patterns” (Brignull, 2023). Dark Patterns are design tactics that purposely exploit cognitive biases of users to get them to make choices that they would not normally make (Bösch et al., 2016). Prior research has indicated that users are somewhat aware of these design choices, but not necessarily completely aware of the extent of the potential harm they can cause (Bongard-Blanchy et al., 2021; Maier & Harr, 2020). Given existing discourse around the potential harm of excessive “screen time”, particularly in the context of the internet, a research gap exists between “Dark Patterns” and the mental health of users (Twenge et al., 2017; Zink et al., 2019).
In this paper, we attempt to identify potentially vulnerable user demographics based on prior research conducted on “Dark Patterns” as well as prior research on mental health and vulnerability within the context of the internet. We will first review prior research done on the links between screen time and user mental health and identify particularly vulnerable users. We specifically choose this context due to the nature of the shared medium of the internet, as well as the fact that research on screen time as well as “Dark Patterns” share similar timeframes. From there we explore research conducted on particularly vulnerable demographics based on analysis from screen time research as well as specific research conducted on internet usage of users with mental health vulnerabilities. Third, we will review research on digital literacy, and the impact of not having it in vulnerable demographics. Fourth, we will review research on “Dark Patterns” and its overall impact on users. Based on this, we will generate research questions to aid in the development of “Dark Pattern” research as well as bring light to its potential consequences for users with vulnerable mental health states. It is hoped that from this that a larger push to combat “Dark Patterns” will occur in the form of education and legislation.
Screen Time
Positive Associations
The nature of screen time research is complex and inconclusive (Tang et al., 2021). Much research has been completed based on cross-sectional studies, which have suggested that increased time spent in front of screens has been associated with increases in mental health issues among young adults. The kind of device and medium may change the impact on users. Certain Demographics also tend to have differing impacts from screen time. Twenge et. al (2017) reported that depressive symptoms and outcomes related to suicide increased from 2010-2015, particularly in female adolescents from grade 8-12. Coyne et. al (2020) reported that a positive association between depressive symptoms and screen time for adolescent females existed, particularly as they began to reach young adulthood.
Negative Associations
Some screen time research has indicated that there may be an inverse effect between screen time and depressive symptoms, in which those with pre-existing conditions are more inclined to spend more time in front of screens (Zink et al., 2017). Brunet et. al (2014) reported that symptoms of depression may predict computer usage in the future in men, but not women.
Vulnerable Demographics
Individuals with Mental Health Concerns
Some research has been conducted on particular interactions for those with pre-existing mental health conditions on the internet. Gak et. al (2022) reported on users with histories of disordered eating, in which they discussed their experiences with targeted weight-loss ads. Participants reported feelings of emotional drain, disruption of recovery, and even physical harm in the form of purchasing supplements offered from the ads. Rose & Dhandayudham (2014) suggested that users who possess common traits of addictive behaviors such as low self-esteem and low self-regulation are likely to engage in impulsive online shopping, due to the enticing and stimulating design of e-commerce websites, as well as the ease of access to the internet. Women were more likely to engage in this behavior.
Marginalized Communities
Marwick & Boyd (2018) suggest that due to cultural differences in interpretation of privacy, marginalized communities such as POC (People of Color) or LGBTQ+ may be subject to exploitation as a result of how they disclose private information, in which they are portrayed negatively, or even face missing out on critical resources due to data collection policies largely created by white Americans.
Digital Literacy
Digital literacy carries significant weight as to how a user may interact with the internet. A lack of literacy, especially in computers can cause anxiety regarding interacting with technology, as well as motivation to learn (Lee & Huang, 2014).
The Elderly
Elderly users, especially in developing countries face exclusion from important resources due to a lack of digital literacy (Mubarak & Suomi, 2022).
Children
Psychologically Vulnerable children who frequent the internet face negative outcomes, but their level of digital literacy may cause inverse outcomes, where lower literacy causes fewer negative outcomes (Helsper & Smahel, 2020).
Dark Patterns
“Dark Patterns” have a large presence on the internet, where several popular applications and websites tend to possess them (Di Geronimo et al., 2020; Mathur et al., 2019). Dark patterns present themselves in a variety of subtle ways. Gray et al. (2016) identified five core dark pattern strategies after completing a comparative analysis of research on dark patterns.
Nagging
The first core strategy is “Nagging”. “Nagging” occurs when “Redirection of expected functionality that persists beyond one or more interactions” (Gray et al. 2016). An example of this could be when you attempt to exit a page on a website, and it repeatedly asks you if you are certain that you wish to leave.
Obstruction
Next is “Obstruction.” “Obstruction” is defined as “Making a process more difficult than it needs to be, with the intent of dissuading certain action(s)” (Gray et al. 2016). For example, blocking out a portion of text on a website that requires a premium subscription to view, despite not expecting to be given a paywall prior to this interaction.
Sneaking
“Sneaking” is defined as “Attempting to hide, disguise, or delay the divulging of information that is relevant to the user” (Gray et al. 2016). For example, when attempting to checkout from an e-commerce website a user may be unaware that a warranty, or additional item was added to their cart without their knowledge.
Interface Interference
“Interface Interference” is defined as “Manipulation of the user interface that privileges certain actions over others” (Gray et al. 2016). For example, the use of particular colors or language choice may give users the illusion that they have no choice when attempting to complete a particular action.
Forced Action
“Forced Action” is defined as “Requiring the user to perform a certain action to access (or continue to access) certain functionality” (Gray et al. 2016). For example, a user may be forced to interact with one or more advertisements to access features of a website or application that is expected to be accessible.
User Emotions
Users often do not even realize that they have encountered these deceptive design patterns until interactions are complete (Maier & Harr, 2020). This leads to feelings of annoyance and pressure. Users also consider these choices to be manipulative (Gray et al., 2021).
Research Questions
Based on the literature reviewed, we can identify demographics that may be at risk because of “Dark Patterns;” Adolescents, the Elderly, and marginalized communities such as POC (People of Color) or LGBTQ+. From this we can pose the following questions:
With the growth of microtransactions and “loot boxes” in video games, adolescents are likely to encounter UI design that is specifically created to incentivize making in-game purchases. A recent study by Zendle & Cairns (2018) suggests evidence for a link between money spent on loot boxes and problem gambling. With this survey being conducted among adults, what could this mean for those even younger?
With a positive correlation between screen time and depressive symptoms in adolescent females potentially being an issue (Coyne et al., 2020), could they be facing even more severe consequences because of interacting with dark patterns?
With the implication that marginalized communities lacking resources compared to White Americans (Marwick & Boyd 2018), could a gap in digital literacy cause negative consequences in the future.
With the implication that a lack of digital literacy could cause anxiety (Lee & Huang, 2014), would mass promoting digital literacy prevent this?