Good news is bad news? News factors on news sharing in three countries
Bibliographic record
Abstract
Social media have played an increasingly important role in the news flow. Amidst several content producers and algorithmic mediation, the news flow on these platforms is enhanced by common users, who share journalistic content through their personal profiles. Based on newsworthiness, a range of research investigate criteria that can help to predict the patterns of news sharing, namely, shareworthiness (TRILLING; TOLOCHKO; BURSCHER, 2017) and how it appears in different contexts. This research investigates the effect of news factors on news sharing on Facebook, in a comparative perspective between Brazil, the United States and Canada, countries with different levels of political stability. To this end, we performed a content analysis of the news published on Facebook during a continuous week in 2016 (n = 1658), by the two news media’s largest pages in each country. They are G1 and Veja (Brazil), Fox News and The New York Times (USA), Global News and CBC News (Canada). Based on Eilder's conception of newsworthiness (2006) and on the state of the art drawn from the empirical studies on news sharing published in the last two decades, we raised six hypotheses about the effect of success, damage, controversy/conflict, influence, proeminence and proximity factors on sharing patterns and the expected differences between countries. The main difference in the sharing patterns of the countries was related to the controversy/conflict factor, corresponding to the context of each country during the sample period. The main similarity was related to the damage factor, that is, negative news. It showed the most stable effect with the greatest statistical significance, which suggests the relevance of this type of news on audience engagement. KEYWORDS: News sharing; Facebook; Newsworthiness
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.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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 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".