Four Key Questions to Guide Human Rights–based Social Listening during Infodemics
Bibliographic record
Abstract
This paper considers what a human rights-based approach to the use of social listening to counter infodemics during a serious health threat might entail, using COVID-19 as a primary example. The paper considers social listening in the context of human rights including health, life, free speech, and privacy, and outlines what a rights-compliant form of social listening to infodemics might entail. The paper argues that human rights offer guardrails against illicit and unethical forms of social listening as well as signposts towards a more equitable, ethical, and effective public health tool. The paper first expands on the human rights dimensions of COVID-19, infodemics, and social listening. Second, it considers the human rights dimensions of social listening in relation to rights to health, life, and free speech, given international human rights law principles for limiting these rights. Finally, using this framework, the paper poses four key questions to frame a rights-based approach to social listening: Why do we listen? How do we listen? Who do we listen to and who is doing the listening? And what are the outcomes of such listening?
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".