Politics and porn: how news media characterizes problems presented by deepfakes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
“Deepfake” is a form of machine learning that creates fake videos by superimposing the face of one person on to the body of another in a new video. The technology has been used to create non-consensual fake pornography and sexual imagery, but there is concern that it will soon be used for politically nefarious ends. This study seeks to understand how the news media has characterized the problem(s) presented by deepfakes. We used discourse analysis to examine news articles about deepfakes, finding that news media discuss the problems of deepfakes in four ways: as (too) easily produced and distributed; as creating false beliefs; as undermining the political process; and as non-consensual sexual content. We provide an overview of how news media position each problem followed by a discussion about the varying degrees of emphasis given to each problem and the implications this has for the public’s perception and construction of deepfakes.
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.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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 | 0.000 |
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 it