Digital Ecosystems of Ideology: Linked Media as Rhetoric in Spanish Political Tweets
Bibliographic record
Abstract
That Twitter is a major form of political mobilization and influence has been well documented. But what is the role of linked media—references to newspapers, photos, videos, and other external sources via URLs—in political Twitter messaging? How are linked references employed as campaign tools and rhetorical devices in messages published by political parties on Twitter? Is there a quantifiable relationship between a party’s ideology and linked media in tweets? With the spread of fake news, threats to a free press, and questioning of the legitimacy of political messaging on the rise globally, the sources on which parties draw to convince voters of their online messaging deserve critical attention. To explore the above questions, this article examines uses of linked media in tweets generated by the official accounts of Spain’s top five political parties during, in the lead-up, and in the immediate aftermath of the Spanish General Elections held on April 28, 2019. Grounded in a corpus of 10,038 tweets collected between March 1 and May 15, 2019, this study quantifies, compares, and critiques how linked media are integrated and remixed into tweets published by the left-leaning Spanish Workers’ Socialist Party (@PSOE), right-wing Popular Party (@populares), left-wing Podemos (@ahorapodemos), neoliberal Citizens (@CiudadanosCs), and far-right Vox (@vox_es) parties. Evidence reveals that each party links to media from somewhat homophilic groups of news outlets, journalists, and public figures, an analysis of which can shed light on how parties construct their digital self-representations, ideological networks of information, and attempt to sway voters.
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How this classification was reachedexpand
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".