Politics and porn: how news media characterizes problems presented by deepfakes
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.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".