The influence of user knowledge and usage behaviour on decision-making and perceived reputation of streaming sites that use dark patterns
Bibliographic record
Abstract
In this paper, we examined how dark patterns (confirmshaming and trick-question), user knowledge, number of services owned and usage frequency impact users' decision-making and service reputation using a subscription-based streaming website as proof-of-concept. Overall, users perceived both patterns as manipulative. However, this negative perception did not adversely impact the perceived trustworthiness and credibility of the website. While in the confirmshaming condition, 68% of those without knowledge of dark patterns selected the expensive plan promoted by the service over the cheap (standard) plan, the reverse is the case among those with knowledge, 35% of whom selected the expensive (premium) plan. This finding indicates that as users become knowledgeable about dark patterns, they are more likely to reject the service-promoted choice, as 40% of knowledgeable users in the trick-question condition edited their initial choice, compared with 10% and 6% in the confirmshaming and control conditions, respectively. Moreover, low-frequency and low-services users in the trick-question condition were most likely to fall for the expensive plan. However, high-frequency and high-services users in the confirmshaming condition were most likely to fall for the expensive plan. The findings highlight the need to raise awareness about dark patterns to prevent unsuspecting users from making financial decisions against their best interest.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".