Amplifying the News: An Analysis of the Factors Driving Republication and Facebook Engagement with News
Bibliographic record
Abstract
This study examines the factors that influence republishing, sharing, and engagement with news in a digital media environment. It does so using a sample of 69 stories about climate emergency preparedness published by The Conversation, which were republished 544 times by 215 media outlets and posted to Facebook (in their original or republished form) 675 times. Using content analyses and regression analyses, we tested the impact of content-related factors—such as news values and the inclusion of systemic vs personal solutions in the stories—on how frequently stories were amplified by republishing media outlets and Facebook users. We also tested the impact of source-related factors—such as whether stories represented original vs republished content, and whether the republishing media outlet represented legacy journalism—on Facebook posting and engagement. Our findings reveal that content- and source-related factors intersect in complex ways to shape which stories gain traction via these two forms of news amplification, pointing to the value of constructive journalism but also the power of a media outlet’s reputation. Moreover, we find that factors influencing republication differ from those impacting Facebook amplification, suggesting that what journalists find newsworthy may differ from what matters to social media audiences.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 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".