Preventative Nudges: Introducing Risk Cues for Supporting Online Self-Disclosure Decisions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Like in the real world, perceptions of risk can influence the behavior and decisions that people make in online platforms. Users of Social Network Sites (SNSs) like Facebook make continuous decisions about their privacy since these are spaces designed to share private information with large and diverse audiences. In particular, deciding whether or not to disclose such information will depend largely on each individual’s ability to assess the corresponding privacy risks. However, SNSs often lack awareness instruments that inform users about the consequences of unrestrained self-disclosure practices. Such an absence of risk information can lead to poor assessments and, consequently, undermine users’ privacy behavior. This work elaborates on the use of risk scenarios as a strategy for promoting safer privacy decisions in SNSs. In particular, we investigate, through an online survey, the effects of communicating those risks associated with online self-disclosure. Furthermore, we analyze the users’ perceived severity of privacy threats and its importance for the definition of personalized risk awareness mechanisms. Based on our findings, we introduce the design of preventative nudges as an approach for providing individual privacy support and guidance in SNSs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 it