A study of intermedia and interorganizational agenda-setting in the news coverage of the Ebola virus on Twitter
Why this work is in the frame
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Bibliographic record
Abstract
The Ebola virus is a rare but often severe and fatal illness in humans. It spreads from animals to humans and then transgresses through human-to-human transmission. The 2014 Ebola virus disease outbreak captured substantial media attention around the world, which is the cornerstone of our study since it can inform us about the current news coverage on the COVID-19 pandemic. This article investigates the media coverage of Ebola in five pairs of English and Arabic international television media outlets (BBC, CNN, SkyNews, RT and France24) by examining the headlines of 298,559 news stories that the respective organizations posted on their official Twitter accounts. Methodologically, we extracted headlines from news outlets that addressed the news on the Ebola virus in two languages: English and Arabic. The media outlets include the following: CNN (English and Arabic), BBC (English and Arabic), SkyNews (English and Arabic), RT (formerly known as Russia Today) (English and Arabic) and France24 (English and Arabic) from late 2013 to early 2015 during which time the Ebola epidemic intensified. We then used descriptive statistics to understand the volume of news coverage and calculate the frequencies, percentages, mean, median and standard deviations for these channels. Further, we continued to model time series regression between the five pairs of news outlets using Granger causality tests. The findings show that over the course of approximately one year’s worth of coverage on these networks, Ebola was mentioned in the headlines of 4138 stories, which constitutes 1.38 per cent of the total news coverage of all media outlets. Building on the theory of intermedia agenda-setting that outlines the ways in which major news organizations influence the agendas of other news outlets, the findings reported here indicate strong, time-ordered patterns where English-language coverage consistently precedes and helps to significantly explain the distribution of Arabic media coverage. In addition to providing evidence of intermedia agenda-setting from a comparative perspective in this context, this article expands on this theory and suggests that it can be applied to multilingual outlets from the same news organizations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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