Enhancing User Awareness of Manipulative Designs: A Study on Persuasive Strategies for Mobile App Platforms
Bibliographic record
Abstract
The rise of e-commerce, gaming, and social networking sites has exposed the use of manipulative designs (MDs), or “dark patterns,” which exploit users’ cognitive biases to benefit companies at the users’ expense. While previous research has classified MDs and explored their impact, there has been limited focus on how to warn and educate users to recognize and resist these tactics. To address this gap, we conducted a controlled study with 135 participants, using a Protection Motivation Theory (PMT) survey to understand what motivates people to learn about manipulative designs (MDs). We also tested two persuasive strategies, based on Cialdini’s principles of social influence and authority, to gauge their effectiveness in raising awareness about MDs. For this, we created a simulated application in a mobile app distribution platform modeled like Google Play Store, containing a visual signal, a warning based on one of the two strategies, and simulated reviews from other users. The results indicate that two of the five PMT constructs - a higher Perceived Severity of MDs and a lower Perceived Response Cost of learning about MDs - have the most significant influence on the Intention to learn more about MDs. The participants in the experimental group, exposed to the two persuasive strategies exhibited a larger increase in their intention to seek information about MDs than the participants in the control group. We also collected and analyzed the time spent on viewing and working with the simulation by participants. Our analysis revealed that the social influence version was relatively more effective than the authority version in capturing participants’ attention. Our study highlights the potential of persuasive interventions in improving user protection against manipulative designs (MDs) on mobile app distribution platforms. Implementing such strategies can increase accountability, transparency, and MD awareness among users, helping them avoid exploitation.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".