Identifying Dark Patterns in User Account Disabling Interfaces: Content Analysis Results
Bibliographic record
Abstract
Dark patterns are user interface (UI) strategies deliberately designed to influence users to perform actions or make choices that benefit online service providers. This mixed methods study examines dark patterns employed by social networking sites (SNSs) with the intent to deter users from disabling accounts. We recorded our attempts to disable experimental accounts in 25 SNSs drawn from Alexa’s 2020 Top Sites list. As a result of our systematic content analysis of the recordings, we identified major types of dark patterns (Complete Obstruction, Temporary Obstruction, Obfuscation, Inducements to Reconsider, and Consequences) and unified them into a conceptual model, based on the differences and similarities within nuanced subtypes in the user account disabling context. The Dark Pattern Typology presented at the 12th International Conference on Social Media and Society is further illustrated in this work. We document the distribution of the subtypes in our sample SNSs, exemplifying dark UI design choices. All of the sites used at least one type of dark pattern. Our findings provide empirical evidence for these pervasive—yet rarely discussed—strategies in the social media industry. Users who wish to discontinue using SNSs—to protect their privacy, break an addiction, and/or improve their general well-being—may find it difficult or nearly impossible to do so. Dark patterns, as common UI design strategies, require further research to determine whether particularly manipulative and user-disempowering varieties may warrant more stringent social media industry regulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".