A comprehensive keyword analysis of online privacy policies
Bibliographic record
Abstract
Online privacy policies are known to have inconsistent formats and incomplete content. They are also hard to understand and do not effectively help individuals to make decisions about the data practices of the online service providers. Several studies have focused on the deficiencies of privacy policies such as length and readability. However, a very limited number of studies have explored the content of privacy policies. This paper aims to shed some lights on the content of these legal documents. To this end, we performed a comprehensive analysis of keywords and content of over 2000 online policies. Policies were collected from variety of websites, application domains, and regulatory regimes. Topic modeling algorithms, such as Latent Dirichlet Allocation, were used for topic coverage analysis. This study also measured the coverage of ambiguous words in privacy policies. Lastly, a method was used to evaluate keyword similarity between privacy policies which belonged to different regulatory framework or applications. The findings suggested that regulations have an impact on the selection of terminologies used in the privacy policies. The results also suggested that European policies use fewer ambiguous words but use more words such as cookie and compliance with the regional regulations. We also observed that the seed keywords extracted for each section of privacy policies were consistently used in all policies regardless of the application domain and regulations.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".