Dark clouds and silver linings: impact of COVID-19 on internet users’ privacy
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
Abstract Objectives To examine the impact of coronavirus disease 2019 (COVID-19) pandemic on the extent of potential violations of Internet users’ privacy. Materials and Methods We conducted a longitudinal study of the data sharing practices of the top 1000 websites in the United States between April 9 and August 27, 2020. We fitted a conditional latent growth curve model on the data to examine the longitudinal trajectory of the third-party data sharing over the 21 weeks period of the study and examine how website characteristics affect this trajectory. We denote websites that asked for permission before placing cookies on users’ browsers as “privacy-respecting.” Results As the weekly number of COVID-19 deaths increased by 1000, the average number of third parties increased by 0.26 (95% confidence interval [CI] 0.15–0.37) P < 0.001 units in the next week. This effect was more pronounced for websites with higher traffic as they increased their third parties by an additional 0.41 (95% CI 0.18–0.64); P < 0.001 units per week. However, privacy respecting websites that experienced a surge in traffic reduced their third parties by 1.01 (95% CI −2.01 to 0); P = 0.05 units per week in response to every 1000 COVID-19 deaths in the preceding week. Discussion While in general websites shared their users’ data with more third parties as COVID-19 progressed in the United States, websites’ expected traffic and respect for users’ privacy significantly affect such trajectory. Conclusions Attention should also be paid to the impact of the pandemic on elevating online privacy threats, and the variation in third-party tracking among different types of websites.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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