European Twitter Networks: Toward a Transnational European Public Sphere?
Bibliographic record
Abstract
In this article, I explore whether and to what degree the discussion of European issues on Twitter remain within nationally bounded communication spaces or whether such a discussion transcends borders and becomes transnationally European. This article explores the interactions formed around Twitter issue publics of European relevance (Schengen and TTIP), with their geographic locations. Out-degree metrics of the interactions conducted (retweets and mentions) under both #schengen and #ttip hashtags are applied. A network of 28 nodes—one for each of the 28 members of the European Union—has been created. In each node, Twitter data collected from each hashtag is embedded, forming six different weighted networks—one for each hashtag—and all six, with the same number of nodes. The networks contain replies, retweets, or quotes of other tweets in the data set (for which location data is available). This article shows with conclusive, empirical evidence, that there is indeed a transnational European public sphere to a certain degree, at least with respect to these topics analyze.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".