Falling for Russian Propaganda: Understanding the Factors that Contribute to Belief in Pro-Kremlin Disinformation on Social Media
Bibliographic record
Abstract
As Russia launched its full-scale invasion of Ukraine in February 2022, social media was rife with pro-Kremlin disinformation. To effectively tackle the issue of state-sponsored disinformation campaigns, this study examines the underlying reasons why some individuals are susceptible to false claims and explores ways to reduce their susceptibility. It uses linear regression analysis on data from a national survey of 1,500 adults (18+) to examine the factors that predict belief in pro-Kremlin disinformation narratives regarding the Russia–Ukraine war. Our research finds that belief in Pro-Kremlin disinformation is politically motivated and linked to users who: (1) hold conservative views, (2) trust partisan media, and (3) frequently share political opinions on social media. Our findings also show that exposure to disinformation is positively associated with belief in disinformation. Conversely, trust in mainstream media is negatively associated with belief in disinformation, offering a potential way to mitigate its impact.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".