Trust, Privacy, and Safety Factors Associated with Decision Making in P2P Markets Based on Social Networks: A Case Study of Facebook Marketplace in USA and Canada
Bibliographic record
Abstract
As peer-to-peer (P2P) marketplaces have grown rapidly, concerns related to trust, privacy, and safety (TPS) have also increased. While previous studies have explored these aspects in various P2P marketplaces, there has been limited research on Facebook Marketplace (FM), which is distinguished by dramatic growth and intricate entanglement with the Facebook social networking site (SNS). To address this knowledge gap, we conducted interviews with 42 FM users in the US and Canada, investigating TPS factors associated with trading decisions. We identified four categories of factors: pre-existing concerns, signals, interactions, and perceived benefits. We uncover the challenges arising from the interplay of these factors, offer design recommendations for SNS–based marketplaces like FM, and suggest directions for future research. Our study advances the understanding of decision-making processes in SNS–based marketplaces, informs future design improvements for such platforms, and ultimately contributes to a better user experience related to trust, privacy, and safety.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".