Users' Strategies for Ensuring Trust, Privacy, and Safety on Facebook Marketplace: Challenges and Recommendations
Bibliographic record
Abstract
We conducted semi-structured interviews with Facebook Marketplace users to gain insights into their strategies for ensuring Trust, Privacy, and Safety (TPS). Our investigation uncovered a range of approaches participants employed. We discovered that users actively sought to convey their trustworthiness to other users while also assessing the trustworthiness of others. Furthermore, they took steps to safeguard their privacy by selectively sharing information and making thoughtful decisions regarding payments. Participants also implemented various strategies to mitigate the risks of physical harm and financial losses, sometimes resulting in preferences that were conflicted between buyers and sellers. Drawing from these findings, we offer recommendations to aid users in evaluating others' trustworthiness, effectively communicating their own trustworthiness, achieving a more optimal balance between privacy and trust, and increasing awareness of potential risks associated with different payment methods.
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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 | 0.000 | 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".