Donetsk don’t tell – ‘hybrid war’ in Ukraine and the limits of social media influence operations
Bibliographic record
Abstract
Many fear that social media enable more potent influence operations than traditional mass media. This belief is widely shared yet rarely tested. We challenge this emerging wisdom by comparing social media and television as vectors for influence operations targeting Ukraine. This article develops a theoretical framework based on media structure, showing how and why decentralized and centralized media offer distinct opportunities and challenges for conducting influence operations. This framework indicates a relative advantage for television in both dissemination and persuasiveness. We test this framework against the Russo-Ukrainian conflict (before the 2022 escalation), contributing new data from a national survey and a new dataset of Telegram activity. We identify fifteen disinformation narratives, and, using statistical analysis, examine correlations between media consumption, audience exposure to, and agreement with, narratives, and foreign policy preferences. To explore causal mechanisms, we follow up with content analysis. Findings strongly support our theoretical framework. While consuming some partisan social media channels is correlated with narrative exposure, there is no correlation with narrative agreement. Meanwhile, consumption of partisan television channels shows clear and consistent correlation. Finally, agreement with narratives also correlates with foreign policy preferences. However, and importantly, findings indicate the overall limitations of influence operations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".