Dark clouds and silver linings: impact of COVID-19 on internet users’ privacy
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.
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 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 | 0.005 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".