The Newspaper Industry in a Changing Landscape The Shift in News Content of Various Newspapers as a Response to the Rise of Social Media
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper examines the association between the rise of social media and the types of news content produced by newspaper outlets. Over the past two decades, the rise of social media has precipitated a decline in the role of traditional newspaper outlets. I present two hypotheses and their ensuing rationale – hypothesis one describes how newspapers may increase hard news content to further consolidate their reader base, while hypothesis two postulates that hard news content will decrease as papers try to regain the readers they lost to social media. Data was collected from two reputable and two less-reputable newspaper outlets to see how they reacted to increases in social media usage and whether their responses varied. For each newspaper outlet, the author identified the number of articles that included keywords drawn from hard news and soft news word banks. Using a ratio of hard to soft news, regression analysis was then performed. After running regression analysis with trend data from the Pew Research Center on the number of US adults with social media accounts, results indicate a moderate negative correlation amongst the two more reputable newspapers and no correlation amongst less reputable newspapers, meaning that the more reputable newspapers tended to decrease hard news content as social media became more popular.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it