Resisting online manipulation: how teens perceive and respond to privacy dark patterns on social media
Bibliographic record
Abstract
Purpose This study examines how teens perceive and respond to privacy-undermining design strategies – or “privacy dark patterns” – on social networking sites (SNSs). Specifically, we sought to ascertain whether teens can identify privacy dark patterns on social media and to determine how teens respond to these patterns, including documenting any strategies they use to resist them. Design/methodology/approach We conducted four virtual focus groups with Canadian teens aged 13 to 17. In breakout rooms, participants guided a research assistant’s actions while the assistant set up a social networking site account. Participants were instructed to make the account as private as possible and consider how the site’s design could influence their choices. Participants then returned to the main Zoom session and discussed the privacy dark patterns they identified and their strategies for resistance. Findings Our results show that teens can identify a wide range of privacy dark patterns and strategies for resistance when instructed to set up a private social media account and look for design strategies that could influence their behavior. However, teens reported low awareness of how interface design could impact their privacy choices before participating in the study. Teens also failed to identify privacy dark patterns that operated by increasing the salience of certain visual elements. Practical implications Educators should ask teens to consider how social media design influences their privacy choices through hands-on activities. However, the effects of these exercises might not persist during teens’ everyday social media use. Originality/value Little research has specifically investigated how teens respond to dark patterns.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".