Securing Democracy: Online Political Advertising Regulations and Practices in the <scp>EU</scp> and its Member States
Bibliographic record
Abstract
ABSTRACT Starting with the Facebook‐Cambridge Analytica scandal and its link to Brexit and the 2016 US elections, the nexus among online political advertising, micro‐targeting, and data‐driven electoral campaigning has revealed its disruptive potential for democracies. While facilitating innovative modes of direct engagement between politics and citizens, online political advertising also allows parties and other political actors to enact strategies that can effectively target highly specific audience segments, with a potential for domestic players with populist agendas or foreign actors to exploit these technologies in order to disrupt public debate and manipulate key electoral processes. However, few countries in Europe introduced a regulation in this respect, to the extent that the digital environment has often been likened to a Wild West. This study has a two‐fold aim. First, it presents an up‐to‐date comparative analysis of the regulation of political advertising in the European Union as well as in individual European countries showing similarities and differences across countries and levels. Second, it provides a descriptive analysis of the way in which domestic political actors used online political advertisements during the 2024 European election campaign exploring which political families use these strategies more frequently and how much they economically invested in advertising tools.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".