The Newspaper Industry in a Changing Landscape The Shift in News Content of Various Newspapers as a Response to the Rise of Social Media
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.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".