A Comparative Study of How People With and Without ADHD Recognise and Avoid Dark Patterns on Social Media

TL;DR

This paper investigates how people with and without ADHD recognise and avoid dark patterns on social networking sites using a web-based, interactive mock SNS in a 2-by-2 factorial design. It finds no major differences in recognition between ADHD and non-ADHD groups, but reveals context-dependent differences in avoidance, with ADHD participants showing distinctive data-disclosure patterns and lower cognitive load in several subscales. The study contributes by (1) providing ADHD-specific insights in SNS dark-pattern encounters, (2) advancing beyond static images to an interactive, temporally trackable environment, and (3) offering an open-source web app for future SNS-based UX research. Together, these findings inform design and policy debates about protecting vulnerable users from deceptive interfaces in real-world digital ecosystems.

Abstract

Dark patterns are deceptive strategies that recent work in human-computer interaction (HCI) has captured throughout digital domains, including social networking sites (SNSs). While research has identified difficulties among people to recognise dark patterns effectively, few studies consider vulnerable populations and their experience in this regard, including people with attention deficit hyperactivity disorder (ADHD), who may be especially susceptible to attention-grabbing tricks. Based on an interactive web study with 135 participants, we investigate SNS users' ability to recognise and avoid dark patterns by comparing results from participants with and without ADHD. In line with prior work, we noticed overall low recognition of dark patterns with no significant differences between the two groups. Yet, ADHD individuals were able to avoid specific dark patterns more often. Our results advance previous work by understanding dark patterns in a realistic environment and offer insights into their effect on vulnerable populations.

Figures (8)

• Figure 1: This figure shows the "Choose Plan" segment of Task 1. On the left, the non-dark pattern version does not emphasise specific interface elements. On the right is the dark-pattern version, which utilises Interface Interference dark patterns to visually highlight the premium account option over the basic alternative.
• Figure 2: This figure shows the "Edit Profile" segment of Task 2. On the left, the non-dark pattern version does not emphasise options for users to enter data. On the right, the dark-pattern version utilises multiple instances of Interface Interference dark patterns to visually highlight the save button and uses Social Engineering and Forced Action patterns to steer the user into sharing more personal data, for instance through a smiley that turns happier the more data is entered.
• Figure 3: This figure illustrates the task flow of Task 1. Both Task Versions are integrated, although distinguished through different outlines and arrow colours. Orange indicates present paths and options only present in the dark pattern Task Version, while blue only exists in the neutral Task Version. Magenta outlines three included checkbox options. Furthermore, present dark patterns are implied through letters representing high-level strategies from gray_building_2024. Colours and symbols are explained in the legend below the diagram.
• Figure 4: This figure follows the task flow of Task 2. As in Figure \ref{['fig:task1-flow-diagram']}, non-dark pattern and dark pattern Task Versions are integrated, while different outlines and arrow colours indicate changes between them. Orange indicates present paths and options only present in the dark pattern Task Version, while blue only exists in the neutral Task Version. Magenta outlines three included checkbox options. Furthermore, present dark patterns are implied through letters representing high-level strategies from gray_building_2024. Colours and symbols are explained in the legend below the diagram.
• Figure 5: This flowchart shows the procedure of our experimental setup. Starting on Prolific from a desktop device, participants are forwarded to ScoSci Survey, where they are asked to enter demographic data and see scenario and persona information before a QR code links them to the two tasks on the web application on their mobile device. After each task, we asked participants to complete the same questionnaires before they were prompted to return to ScoSci Survey to receive a completion code that allowed them to finish the study on Prolific.