A new study by researchers from Princeton University and the University of Chicago suggests that shopping websites are abundant with dark patterns that rely on consumer deception. The researchers conducted a large-scale study, analyzing almost 53K product pages from 11K shopping websites to characterize and quantify the prevalence of dark patterns. They discovered 1,841 instances of dark patterns on shopping websites, which together represent 15 types of dark patterns.
Note: All images in the article are taken from the research paper.
What are dark patterns
Dark patterns are generally used by shopping websites as a part of their user interface design choices. These dark patterns coerce, steer, or deceive users into making unintended and potentially harmful decisions, benefiting an online service. Shopping websites trick users into signing up for recurring subscriptions and making unwanted purchases, resulting in concrete financial loss. These patterns are not just limited to shopping websites, and find common applications on digital platforms including social media, mobile apps, and video games as well. At extreme levels, dark patterns can lead to financial loss, tricking users into giving up vast amounts of personal data, or inducing compulsive and addictive behavior in adults and children.
Researchers used a web crawler to identify text-based dark patterns
The paper uses an automated approach that enables researchers to identify dark patterns at scale on the web. The researchers crawled 11K shopping websites using a web crawler, built on top of OpenWPM, which is a web privacy measurement platform. The web crawler was used to simulate a user browsing experience and identify user interface elements. The researchers used text clustering to extract recurring user interface designs from the resulting data and then inspected the resulting clusters for instances of dark patterns.
The researchers also developed a novel taxonomy of dark pattern characteristics to understand how dark patterns influence user decision-making. Based on the taxonomy, the dark patterns were classified basis whether they lead to an asymmetry of choice, are covert in their effect, are deceptive in nature, hide information from users, and restrict choice. The researchers also mapped the dark patterns in their data set to the cognitive biases they exploit. These biases collectively described the consumer psychology underpinnings of the dark patterns identified.
They also determine that many instances of dark patterns are enabled by third-party entities, which provide shopping websites with scripts and plugins to easily implement these patterns on their websites.
Key stats from the research
- There are 1,841 instances of dark patterns on shopping websites, which together represent 15 types of dark patterns and 7 broad categories.
- These 1,841 dark patterns were present on 1,267 of the 11K shopping websites (∼11.2%) in their data set. Shopping websites that were more popular, according to Alexa rankings, were more likely to feature dark patterns.
- 234 instances of deceptive dark patterns were uncovered across 183 websites
- 22 third-party entities were identified that provide shopping websites with the ability to create dark patterns on their sites.
Dark pattern categories
Attempting to misrepresent user actions. Delaying information that users would most likely object to once made available.
- Sneak into Basket: The “Sneak into Basket” dark pattern adds additional products to users’ shopping carts without their consent
- Hidden Subscription: Dark pattern charges users a recurring fee under the pretense of a one-time fee or a free trial
- Hidden Costs: Reveals new, additional, and often unusually high charges to users just before they are about to complete a purchase.
Imposing a deadline on a sale or deal, thereby accelerating user decision-making and purchases.
- Countdown Timers: Dynamic indicator of a deadline counting down until the deadline expires.
- Limited-time Messages: Static urgency message without an accompanying deadline
Using visuals, language, or emotion to direct users toward or away from making a particular choice.
- Confirmshaming: It uses language and emotion to steer users away from making a certain choice.
- Trick Questions: It uses confusing language to steer users into making certain choices.
- Visual Interference: It uses style and visual presentation to steer users into making certain choices over others.
- Pressured Selling: It refers to defaults or often high-pressure tactics that steer users into purchasing a more expensive version of a product (upselling) or into purchasing related products (cross-selling).
Influencing users’ behavior by describing the experiences and behavior of other users.
- Activity Notification: Recurring attention grabbing message that appears on product pages indicating the activity of other users.
- Testimonials of Uncertain Origin: The use of customer testimonials whose origin or how they were sourced and created is not clearly specified.
Signalling that a product is likely to become unavailable, thereby increasing its desirability to users. Examples such as Low-stock Messages and High-demand Messages come under this category.
- Low-stock Messages: It signals to users about limited quantities of a product
- High-demand Messages: It signals to users that a product is in high demand, implying that it is likely to sell out soon.
Making it easy for the user to get into one situation but hard to get out of it. The researchers observed one type of the Obstruction dark pattern: “Hard to Cancel”. The Hard to Cancel dark pattern is restrictive (it limits the choices users can exercise to cancel their services). In cases where websites do not disclose their cancellation policies upfront, Hard to Cancel also becomes information hiding (it fails to inform users about how cancellation is harder than signing up).
Forcing the user to do something tangential in order to complete their task. The researchers observed one type of the Forced Action dark pattern: “Forced Enrollment” on 6 websites.
Limitations of the research
The researchers have acknowledged that their study has certain limitations.
- Only text-based dark patterns are taken into account for this study. There is still work needed to be done for inherently visual patterns (e.g., a change of font size or color to emphasize one part of the text more than another from an otherwise seemingly harmless pattern).
- The web crawling lead to a fraction of Selenium crashes, which did not allow researchers to either retrieve product pages or complete data collection on certain websites.
- The crawler failed to completely simulate the product purchase flow on some websites.
- They only crawled product pages and checkout pages, missing out on dark patterns present in other common pages such as the homepage of websites, product search pages, and account creation pages.