#Fyp: Dåliga Nyheter med Krimtema : En studie om nyhetsurval och nyhetsvärdering på TikTok
Bibliographic record
Abstract
Aftonbladet, Expressen and TV4 Nyheterna are the largest Swedish news media on TikTok, and this essay focuses on how these media outlets evaluate news and what the news selection looks like on the platform. We proceeded from the theory of commercialization, news evaluation and agenda setting in combination with previous research on news evaluation and news selection on social media to answer our questions. A method triangulation worked well in this essay, where the quantitative part answered the question of news selection, while the qualitative part answered the question of news evaluation. The quantitative part is based on 300 TikTok clips and the qualitative part is based on 5 interviews with journalists. The results show that crime is the most common news genre on the app and that the news evaluation is greatly influenced by the target group and the design of the platform. The result also showed that bad and surprising news are the most recurring news values according to the news selection and that news that takes place in Sweden are the most common. Social, political, and economic issues are the news that engages the audience the most and can be seen in the number of shares, likes and comments. The commercialization of journalism is also prominent in the journalists' evaluation of the news on TikTok, as well as which news is published. However, further research is required to understand the relationship between news rating and news selection on TikTok, as well as additional aspects such as framing and angle.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.013 |
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".