The TikTok Effect: How Storytelling in Short-Form Videos Shape Discourses on Refugees in Canada—A Mixed Methods Approach
Bibliographic record
Abstract
Despite the growing significance of TikTok as a platform for refugee narratives, substantial knowledge gaps remain in understanding its impact on public opinion, particularly in the Canadian context. This study investigates how refugee narratives on TikTok influence Canadian public opinion compared to traditional media. Employing a mixed-methods design, the research combines content analysis of 500 refugee created TikTok videos (2022–2024) with an experimental survey of 500 demographically stratified Canadians. The content analysis revealed that resilience (64%) and cultural hybridity (55%) dominated refugee narratives, with humor (42%), nostalgia (33%), and collaborative features like duets (29%) driving higher engagement (e.g., 21.3k views for duets). Trauma-centric or policy-critical content received significantly less visibility. The experimental survey exposed participants to TikTok videos, traditional media articles, or neutral content, measuring shifts via the Refugee Perception Index (RPI). TikTok exposure yielded substantial empathy gains and reduced threat perceptions (p < 0.001), whereas traditional media elicited marginal empathy increases and heightened threat perceptions. Regression analysis identified humor (β = 0.42), duets (β = 0.38), and nostalgia (β = 0.35) as key predictors of empathy. Younger and politically liberal participants exhibited stronger positive shifts. This study is significant as it shows promise for addressing knowledge gaps as it relates to how new media platforms shape refugee narratives globally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".