User modelling for privacy-aware self-disclosure
Bibliographic record
Abstract
Information and Communications Technology (ICT) is proliferating exponentially and has undoubtedly become an intrinsic part of our daily lives. However, its fast-paced growth has brought upon multiple challenges amongst which are the human-centric threats to cybersecurity and privacy. One of the main reasons for this is the shift in individuals’ behaviour towards carelessly disclosing private information, especially on social media.This work builds on the existing literature that identifies the motivations leading to oversharing in order to predict and mitigate self-disclosure. This paper aims to tackle this first by proposing a user model for the individual’s disclosure motivations. The aim is to measure how driven the user is to share personal information given a specific context. This is paramount to second objective, which is designing personalized privacy-preserving interventions known as nudges based on the user model. A visual aid is provided to further attract the user’s attention and persuade them to alter their behaviour. Study participants (N=800) were recruited via Mechanical Turk and responded to realistic scenarios to assess their motivations for sharing personal data. Then, persuasive nudges were pushed in the context of the evaluation.
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.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".