Unbiased misinformation policies sanction conservatives more than liberals
Bibliographic record
Abstract
In response to intense pressure from policy makers and the public, technology companies have enacted a range of policies aimed at reducing the spread of misinformation online1-3. The enforcement of these policies has, however, led to technology companies being regularly accused of political bias4-6. We argue that even under politically neutral anti-misinformation policies, such political asymmetries in enforcement should be expected, as there is a political asymmetry in the sharing of misinformation7-12. We support this argument with an analysis of Twitter data from 9,000 politically active users during the U.S. 2020 presidential election. While users on the political right were indeed substantially more likely to be suspended than those on the left, users on the right also shared far more links to low quality news sites – even when news quality was determined by politically-balanced groups of laypeople, or groups of only Republicans – and were estimated to have a far higher likelihood of being bots. We find similar associations between conservatism and low quality news sharing (based on both expert and politically-balanced layperson ratings) in seven other datasets of sharing from Twitter, Facebook, and survey experiments, spanning 2016 to 2023. These results demonstrate that political imbalance in enforcement need not imply bias, and should not dissuade technology companies from taking action against the spread of misinformation.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".