Public Beliefs about Falsehoods in News
Bibliographic record
Abstract
The circulation of misinformation, lies, propaganda, and other kinds of falsehood has, to varying degrees, become a challenge to democratic publics. We are interested in the question of what publics believe about their own exposure to falsehoods in news, and about what contributes to similarities and differences in these beliefs across countries. We are also interested in the question of whether publics report attempting to verify news that is suspect to them. Here we report on a comparative election survey in the United States, the United Kingdom, and France. We find three key predictors of publics’ beliefs that they have been exposed to falsehoods: discussion of news, use of social media for political purposes, and exposure to counter-attitudinal information. The nexus between these three predictors and beliefs about falsehoods exists in all three countries, as we anticipate that it likely exists elsewhere. We do not find voters on the right to be different from those on the left in the United Kingdom and France, but do find a substantial difference in the United States, which is likely due to the 2016 Trump campaign. We conclude with concerns about the imbalance in beliefs about exposure to falsehoods in the United States and the apparent capacity of a single leader, in the right context, to shape public beliefs about what is to be believed.
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".