Discursive dynamics and local contexts on Twitter: The refugee crisis in Europe
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 today’s hybrid media environment, traditional news organizations extend their presence on Online Social Networks (OSNs) and compete with political and civil society organizations, public figures, and other influential digital storytelling individuals. This article examines conversations on Twitter, one of the most widely used OSNs, about Europe’s refugee crisis in 2014 and 2015. We use, in particular, topic modeling techniques to deduce the existence of a complex network of Twitter topics formed in response to coverage of and opinion formation surrounding the European refugee crisis. We collected more than 11 million tweets in six different languages. One of our most significant findings is that while most conversations happen in English, the refugee crisis has had different rhythms in other languages. Our assumption is that this could be evidence that the power of mainstream local media on Twitter to set the agenda is considerable, at least regarding refugee-related conversations in Europe.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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