Algorithmic curation of News on YouTube: Evidence from the 2022 French Presidential Campaign
Bibliographic record
Abstract
Debate is growing over how algorithmic recommendations influence news visibility, particularly regarding interest and ideological bias. This study examines YouTube's News Recommender System (NRS), focusing on the French "News" section of the homepage through a large-scale audit using automated browsing agents.Our findings show that the NRS prioritises platform-native creators over established news outlets, favouring content that aligns with YouTube's features rather than traditional editorial standards. Politically charged, opinion-based videos, especially those from prominent figures affiliated with extreme political parties, receive ongoing algorithmic promotion. While centrist and moderate political figures remain underrepresented, the algorithm boosts their visibility once users interact with this type of content. This two-part mechanism, which amplifies already prominent content and appears to overcompensate for rare content, does not just reflect engagement-based optimisation, but is driven by the algorithm's tendency to maintain coherence in a highly unbalanced content landscape. However, this compensatory logic does not counteract the algorithm's broader tendency to promote content from radical political figures while marginalising institutional news outlets. Through this process, the NRS actively reshapes news exposure within the "News" section, privileging political expression over journalistic authority and reproducing structural hierarchies of visibility.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".