Disinformation and Russia-Ukrainian War on Canadian Social Media
Bibliographic record
Abstract
The Russia-Ukrainian war has led to a large disinformation campaign, largely spread through social media. Canada has been a target of these influence campaigns to affect Canadian public opinions. In this policy brief, we venture to examine the prevalence of pro-Russian narratives on Canadian social media as well as identify major influencers creating and spreading such narratives. Additionally, using artificial intelligence, we seek to examine the reach and nature of pro-Russian disinformation narratives. Our research team has been collecting more than 6.2 million Tweets globally since January 2022 to monitor and measure Russian influence operations on social media. We find that pro-Russian narratives promoted in the Canadian social media ecosystem on twitter are divided into two large communities:1) accounts influenced by sources from the United States and 2) those largely influenced by sources from international sources from Russia, Europe, and China. First, pro-Russian discourse on Canadian Twitter blames NATO for the conflict suggesting that Russia’s invasion was a result of NATO’s expansionism or aggressive intentions toward Russia. In this context, pro-Russian propaganda argues that the West has no moral high ground to condemn the invasion and nations such as Canada, the US, and the UK are trying to force Europe into this conflict to benefit materially. Second, it is suggested that Western nations are propping up fascists in Ukraine, thus justifying Russia’s actions. Thirdly, pro-Russian narrative attempts to amplify mistrust of democratic institutions, be it the media, international institutions, or the Liberal government. Faced with the challenges associated with foreign interference, it is important to gain a deeper understanding of the spread of disinformation in Canada.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".