Evaluating depolarization-oriented news ranking strategies using LLM-generated articlesSergaš, Uroš (Avtor) Tkalčič, Marko (Avtor) Ferwerda, Bruce (Avtor) news recommendationpolitical polarizationaffective polarizationranking strategieslarge language modelsAlgorithmic news ranking and large language models (LLMs) increasingly mediate how citizens receive political information. While personalization is often criticized for reinforcing echo chambers, ranking has also been proposed as a lever for depolarization by shaping exposure to cross-cutting viewpoints. We test this claim in an online experiment (N=100, Prolific) using five LLM-generated news articles on gun legislation spanning pro–gun freedom to pro–gun control. All participants read the same articles; the only manipulation was article order, instantiated as a random baseline and six stance-aware depolarization-oriented strategies (counter-narrative sandwich, balanced alternation, and directional gradients). Pre–post questionnaires measured ideological self- placement (feeling thermometer) and affective evaluations of gun-control and gun-freedom advocates. Across six research questions, we find no statistically reliable evidence that any ranking strategy reduces ideological or affective polarization relative to the baseline, nor that stance-conditioned assignment improves outcomes. These results suggest that, under the constraints of a short, single-session, single-topic exposure study with LLM-generated article stimuli, article ordering alone did not produce detectable depolarization effects. The findings motivate future work to design longer-term, multi-topic, and more interactive solutions for mitigating affective polarization.20262026-06-02 11:54:13Neznano23090UDK: 004.8ISSN pri članku: 1613-0073OceCobissID: 280125443COBISS.SI-ID: 280133891sl