An investigation of media reports of digital surveillance within the first year of the COVID-19 pandemic
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic prompted a surge in digital public health surveillance worldwide, with limited opportunities to consider the effectiveness or impact of digital surveillance. The news media shape public understanding of topics of importance, contributing to our perception of priority issues. This study investigated news media reports published during the first year of the pandemic to understand how the use and consequences of digital surveillance technologies were reported on. Methods: A media content analysis of 34 high- to low-income countries was completed. The terms "COVID-19," "surveillance," "technologies," and "public health" were used to retrieve and inductively code media reports. Results: Of the 1,001 reports, most were web-based or newspaper sources on the development and deployment of technologies directed at contact tracing, enforcing quarantine, predicting disease spread, and allocating resources. Technology types included mobile apps, wearable devices, "smart" thermometers, GPS/Bluetooth, facial recognition, and security cameras. Repurposed data from social media, travel cards/passports, and consumer purchases also provided surveillance insight. Media reports focused on factors impacting surveillance success (public participation and data validity) and the emerging consequences of digital surveillance on human rights, function creep, data security, and trust. Discussion: Diverse digital technologies were developed and used for public health surveillance during the first year of the COVID-19 pandemic. The use of these technologies and witnessed or anticipated consequences were reported by a variety of media sources worldwide. The news media are an important public health information resource, as media outlets contribute to directing public understanding and shaping priority public health surveillance issues. Our findings raise important questions around how journalists decide which aspects of public health crises to report on and how these issues are discussed.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".