Social media framing of the 2022 ‘War in Ukraine’: A content analysis study of the Canadian prime minister’s tweets
Bibliographic record
Abstract
This article scrutinizes the frames that are deployed by the Canadian prime minister Justin Trudeau about the Russia–Ukraine event and the actors who are involved in this event. Accordingly, the corpus consists of 108 English text-only original tweets retrieved from the Twitter account of Trudeau (@JustinTrudeau) between 23 February and 30 April 2022. This timeframe covers the beginning and the early stages of the Russia–Ukraine event that has captured the attention of mainstream and social media. Qualitative content analysis is conducted on the selected tweets, guided by framing theory and critical discourse analysis. The findings reveal that Trudeau utilized different frames to label and portray the current event. He also used ‘authoritarian’ frames to depict Russia and pro-Russia actors as outgroup members, who are directly responsible and should be held accountable for their actions, whereas ‘freedom’ frames are employed to represent Ukraine and pro-Ukraine actors as ingroup members, who have common values with Canada and need all kinds of support and assistance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".