Reasons, Rewards, Regrets: Privacy Considerations in Location Sharing as an Interactive Practice
Bibliographic record
Abstract
Rapid growth in the usage of location-aware mobile phones has enabled mainstream adoption of location-sharing services (LSS). Integration with social-networking services (SNS) has further accelerated this trend. To uncover how these developments have shaped the evolution of LSS usage, we conducted an online study (N = 362) aimed at understanding the preferences and practices of LSS users in the US. We found that the main motivations for location sharing were to connect and coordinate with one's social and professional circles, to project an interesting image of oneself, and to receive rewards offered for 'checking in.' Respondents overwhelmingly preferred sharing location only upon explicit action. More than a quarter of the respondents recalled at least one instance of regret over revealing their location. Our findings suggest that privacy considerations in LSS are affected due to integration within SNS platforms and by transformation of location sharing into an interactive practice that is no longer limited only to finding people based on their whereabouts. We offer design suggestions, such as delayed disclosure and conflict detection, to enhance privacy-management capabilities of LSS. Copyright is held by the author/owner.
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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 | 0.019 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".