UKTwitNewsCor: A Dataset of Online Local News Articles for the Study of Local News Provision
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
In this paper, we present UKTwitNewsCor, a comprehensive dataset for understanding the content production, dissemination, and audience engagement dynamics of online local media in the UK. It comprises over 2.5 million online news articles published between January 2020 and December 2022 from 360 local outlets. The corpus represents all articles shared on Twitter by the social media accounts of these outlets. We augment the dataset by incorporating social media performance metrics for the articles at the tweet level. We further augment the dataset by creating metadata about content duplication across domains. Alongside the article dataset, we supply three additional datasets: a directory of local media web domains, one of UK Local Authority Districts, and one of digital local media providers, providing statistics on the coverage scope of UKTwitNewsCor. Our contributions enable comprehensive, longitudinal analysis of UK local media, news trends, and content diversity across multiple platforms and geographic areas. In this paper, we describe the data collection methodology, assess the dataset geographic and media ownership diversity, and outline how researchers, policymakers, and industry stakeholders can leverage UKTwitNewsCor to advance the study of local media.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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