Drivers and persuasive strategies to influence user intention to learn about manipulative design
Bibliographic record
Abstract
The proliferation of e-commerce, game, and social networking sites, has brought to light the use of "dark patterns" or, more generally, manipulative designs (MDs), which exploit psychological effects and cognitive biases of users to channel their behavior toward outcomes that benefit the company or owner of the site, against the users’ best interests. Previous research has categorized MDs, assessed their impact on users, gauged their prevalence, and attempted automated detection using computer vision and natural language processing techniques. However, limited attention has been given to understanding how to warn and educate users about MDs, guiding them to recognize and resist such manipulative tactics. To address this gap, we carried out a controlled study with n=134 participants, using a survey based on the Protection Motivation Theory (PMT) to better understand the motivations of people to learn about MDs. We also explored the effectiveness of two persuasive strategies, based on Cialdini’s principles of influence (social influence and authority), to trigger attention towards MDs and intention to learn more about MDs and to avoid them. 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. Our study showcases the potential of a persuasive intervention, illustrating how mobile app distribution platforms can enhance user protection against MD exploitation. By implementing such interventions, these platforms can boost accountability and transparency of applications existing on their platform, and MD awareness among their users.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".