Is pro-Kremlin Disinformation Effective? Evidence from Ukraine
Bibliographic record
Abstract
Can residents of Ukraine discern between pro-Kremlin disinformation and true statements? Moreover, which pro-Kremlin disinformation claims are more likely to be believed, and by which audiences? We present the results from two surveys carried out in 2019—one online and the other face-to-face—that address these questions in Ukraine, where the Russian government and its supporters have heavily targeted disinformation campaigns. We find that, on average, respondents can distinguish between true stories and disinformation. However, many Ukrainians remain uncertain about a variety of disinformation claims’ truthfulness. We show that the topic of the disinformation claim matters. Disinformation about the economy is more likely to be believed than disinformation about politics, historical experience, or the military. Additionally, Ukrainians with partisan and ethnolinguistic ties to Russia are more likely to believe pro-Kremlin disinformation across topics. Our findings underscore the importance of evaluating multiple types of disinformation claims present in a country and examining these claims’ target audiences.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".