| Title: | Evaluating depolarization-oriented news ranking strategies using LLM-generated articles |
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| Authors: | ID Sergaš, Uroš (Author)
ID Tkalčič, Marko (Author)
ID Ferwerda, Bruce (Author) |
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| Files: |  RAZ_Sergas_Uros_2026.pdf (1,09 MB)
MD5: 3BECEB61C082BB3F97C8A90EA6D511B2
RAZ_Sergas_Uros_2026.pdf (1,09 MB)
MD5: 3BECEB61C082BB3F97C8A90EA6D511B2
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| Language: | English |
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| Work type: | Unknown |
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| Typology: | 1.08 - Published Scientific Conference Contribution |
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| Organization: | FAMNIT - Faculty of Mathematics, Science and Information Technologies
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| Abstract: | Algorithmic 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. |
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| Keywords: | news recommendation, political polarization, affective polarization, ranking strategies, large language models |
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| Publication version: | Version of Record |
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| Year of publishing: | 2026 |
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| Number of pages: | Str. 118-126 |
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| PID: | 20.500.12556/RUP-23090  |
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| UDC: | 004.8 |
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| ISSN on article: | 1613-0073 |
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| COBISS.SI-ID: | 280133891 |
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| Publication date in RUP: | 02.06.2026 |
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| Views: | 137 |
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| Downloads: | 6 |
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