DANGEROUS NEW TIKTOK TREND – Sensational Language! A Qualitative Discourse Analysis of TikTok and Facebook Through Similar Scandals
Bibliographic record
Abstract
This study examines Canadian media coverage of TikTok and Facebook amid similar scandals: TikTok's ban from government devices in March 2023 and Facebook's whistleblower, Frances Haugen, scandal in October 2021. Using Bacchi's "What is the Problem Represented to Be" approach, a qualitative content study of 51 Canadian news stories (37 TikTok, 14 Facebook) was completed to see how the media talked about the associated scandals and risks of each platform. The results show that the majority of news coverage of TikTok in Canada was negative. It used sensational language, focused on the app's links to China, and framed it as a direct and intentional threat, particularly to youth. Facebook coverage, on the other hand, was less common overall, despite similar security concerns. It often represented the platform to be accidentally negligent instead of intentionally harmful. The differences are discussed through the framework of moral panic, youth-focused technopanics, and Canada's extensive history of both latent and outwardly expressed anti-Asian sentiment. The study shows how historical biases, geopolitical narratives, and youth moral panic potentially influence Canadian media’s use of sensational language, in turn impacting the coverage of social media platforms and potentially shaping public perception and policy 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.031 | 0.027 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".