Framing the Gaza Crisis: A Comparative Analysis of Trudeau’s and Biden’s Social Media Narratives
Bibliographic record
Abstract
The Gaza Strip crisis, particularly following the October 7, 2023, escalation has garnered global attention and elicited a diverse range of political responses. As state leaders increasingly rely on digital platforms to communicate their positions, understanding how crises are framed on social media has become crucial for research in political communication. This study applies framing theory to analyze how former Canadian Prime Minister Justin Trudeau and former U.S. President Joe Biden framed the Gaza Strip crisis through their official X (formerly Twitter) accounts. Using a qualitative framing analysis, the study examines 112 original tweets – 45 from Trudeau and 67 from Biden – posted between October 7, 2023, and January 19, 2025. The research identifies key framing strategies, including problem definition, causal interpretation, moral evaluation, and treatment recommendations, to assess how both leaders framed the crisis's humanitarian, political, and security dimensions. Findings reveal both convergences and divergences in their framing strategies. Both leaders emphasized the humanitarian crisis, advocating for the application of international law, humanitarian assistance, and the establishment of ceasefires. However, while Trudeau's framing prioritized humanitarian concerns and diplomacy, Biden's tweets placed greater emphasis on terrorism, security, and Israel's right to self-defense. Trudeau's messaging was often more conciliatory, aligning with Canada's tradition of peacekeeping and humanitarian aid, while Biden's framing reflected the United States' strategic alliances and counterterrorism priorities. This study contributes to the growing body of research on political communication in digital spaces by demonstrating how social media serve as a strategic platform for crisis framing, diplomacy, and shaping public opinion. The findings underscore the influence of digital political framing in shaping global narratives and policy debates. The study concludes by proposing recommendations for future research on the evolving role of social media in political communication.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".