Thematic Analysis of Foreign State-Led Disinformation Narratives: A Canadian Perspective
Bibliographic record
Abstract
State-sponsored disinformation campaigns threaten democratic resilience, public trust, and geopolitical stability. Foreign actors, particularly Russia, China, and Iran, exploit digital platforms to manipulate narratives and influence public perception. This research employs a dual-approach framework, integrating qualitative thematic analysis with AI-driven computational methods to detect, classify, and track disinformation narratives. While traditional qualitative analysis provides valuable insights, it lacks scalability. To address this, we enhance human analysis with AI-based methodologies, including natural language processing (NLP), machine learning (ML), and network analysis techniques. Using BERT, Random Forest, and Latent Dirichlet Allocation (LDA), we classify disinformation posts, quantify their prevalence, and track narrative evolution. Our findings reveal that foreign actors adapt dynamically, leveraging deepfake technologies and bot-driven amplification. By combining AI-driven analytics with expert-driven qualitative research, this research presents a scalable detection framework, providing actionable intelligence for policymakers, regulators, and fact-checkers to mitigate the risks posed by foreign 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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".