Medias rapportering av AI: Framtidstro eller dystopi? : En kvantitativ innehållsanalys av texter publicerade av media i relation till AI och dess utveckling.
Bibliographic record
Abstract
Problem Statement and Purpose: In recent years, skepticism towards AI has increased among Swedes. This study aims to investigate whether there is a correlation between this skepticism and how AI is portrayed by the media. The purpose is to present a depiction of how AI and its development are portrayed in the media, as well as to examine whether reinforcing images are used more or less depending on the nature of the news. Additionally, the study aims to explore differences between public and commercial media's reporting on AI. Method and Material: The chosen method for this study is a quantitative content analysis. A total of 251 publications were analyzed, all from Aftonbladet, Dagens Nyheter, SVT, and Sveriges Radio. The publications are from the first quarter of 2023 and 2024 and were sourced from the media archive Research Retriever. After collection, the material was analyzed using the statistical analysis tool SPSS. Main Results: There is a correlation with the widespread skepticism as media reporting on AI is predominantly negative. However, the results indicate that media reporting was somewhat more positive in 2024 compared to 2023. One reason for the increasing skepticism, despite this, may be that according to Agenda Setting Theory, the media has highlighted AI as particularly important to the public. As the negative angle remains dominant, the impression is that it is something to be concerned about. The results show that the difference between public service and commercial media in how they portray AI development is minimal. Profit-driven media tends to publish more frequently. Regarding the use of reinforcement images, it can be concluded that most publications are accompanied by some associated image. Public service uses more positive reinforcement images than commercial actors, according to the study's results. Commercial actors more frequently use reinforcement images in negatively angled articles compared to public service.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".