Exploring TikTok’s Potential as a Platform for Valuable Journalism
Bibliographic record
Abstract
News organizations have begun incorporating the social media app TikTok as one of the many platforms they post their content to. In Canada, since the passing of the Online News Act (Bill C-18) in summer 2023, Meta, the company that owns Facebook and Instagram, has blocked Canadian news outlets from posting content on their platforms, leading many to turn to TikTok to reach audiences on social media. The app's popularity and lack of transparency raise questions about whether valuable journalism could be possible on the app. This research-creation thesis uses Irene Costera Meijer's (2022) "valuable journalism" concept, which describes three experiences that may lead individuals to feel a sense of value towards a news story: (1) getting recognition, (2) increasing mutual understanding, and (3) learning something new. Guided by these concepts, this research analyzes three Canadian commercial mainstream news outlets’ TikTok accounts—namely CTV News, Global News, and CityNews Toronto—to explore whether their content exhibits elements of these "valuable journalism" experience. The total number of TikTok videos analyzed in this research is 542 across all three news outlets' TikTok accounts between August 11 and November 11, 2023. The results show a progressive use of TikTok and overall evidence of valuable experiences according to Costera Meijer's concepts, but also suggests more can be done to creatively produce news on the app using its tailored tools, and to reach audiences in valuable ways. These findings helped inform the creation of three original TikTok videos that each seek to demonstrate the elements of Costera Meijer's three valuable news experiences, to bridge these theoretical aspects to practical elements of journalism production for social media.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".