Youth Perspectives on Privacy Dark Patterns
Bibliographic record
Abstract
Privacy dark patterns are user interface (UI) design strategies intended to influence users to make choices that reduce their online privacy. This integrated article dissertation documents privacy dark patterns on social networking sites (SNSs) popular with teen users, assesses how teens perceive and respond to these tactics, and critically evaluates the efficacy of privacy regulation across different jurisdictions in protecting individuals from manipulative interface design. Privacy dark patterns on SNSs popular with teens were documented by recording and content-analyzing the process of setting up an account on five sites (Snapchat, TikTok, Instagram, Twitter, and Discord) for evidence of UI design strategies that undermine users’ institutional and/or social privacy. In total, three major types (Obstruction, Obfuscation, and Pressure) and 10 subtypes of privacy dark patterns were identified, including a pattern that has not been documented in prior research (False “Private” Account). Teen perspectives of privacy dark patterns were obtained by holding four virtual focus groups with Canadian teens aged 13-17 (n=38) on the topic of social media design. This study found that teens can identify privacy dark patterns and strategies for resistance when explicitly asked to set up a private social media account for a popular SNS (TikTok, Twitter, or Instagram) and look for design tactics that could influence their choices. However, teens reported low awareness of how social media design could influence their behaviour prior to taking part in the focus groups and failed to identify dark patterns that operated by giving certain options greater visual salience than others. Finally, three regulatory frameworks (the Personal Information Protection and Electronic Documents Act, the California Consumer Privacy Act, and the General Data Protection Regulation) were evaluated for their efficacy in protecting individuals from the effects of privacy dark patterns. This analysis found that less explicit and extensive protection is afforded to users against Obstruction and Pressure dark patterns compared to Obfuscation tactics, especially in a Canadian regulatory context. This research can inform the development of educational and regulatory countermeasures to dark patterns that support teens in making conscious and informed choices about the collection and use of their personal data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".