Media talks Privacy: Unraveling a Decade of Privacy Discourse around the World
Bibliographic record
Abstract
Our increasingly digital world has heightened concerns about privacy. Newspaper and media reporting influences and shapes public opinion, which impacts the strategic and operational decisions of a variety of stakeholders, making it crucial to understand how privacy-related issues are portrayed in the media. Leveraging time-series analysis, topic modeling, and sentiment analysis, this paper presents a comprehensive study on the coverage of privacy-related issues in newspapers from 2010 to 2022 across six regions of the world. Temporal trends in privacy coverage reveal a gradual increase in attention to privacy issues globally, with a notable surge observed in newspapers from the Global South, complementing the historically prominent Global North coverage. Topic modeling uncovers dominant themes in privacy reporting, revealing shifts in media focus from government surveillance to data breaches and tech corporations' role. Notably, the majority of privacy reporting carries a negative sentiment, emphasizing the widespread unease that pervades discussions surrounding privacy matters.
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How this classification was reachedexpand
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".