# Balancing Domestic and Global Perspectives: Evaluating Dual-Calibration and LLM-Generated Nudges for Diverse News Recommendation

arXiv: https://arxiv.org/abs/2603.05780 · HTML: https://mlsanity.com/paper/2603.05780/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 81 references that are in the map.
- First posted on arXiv 6 Mar 2026.

## Builds on (most cited first, 12 of 12)

- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy](https://mlsanity.com/paper/2002.04087/) (2020)
- [The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation](https://mlsanity.com/paper/2008.09273/) (2020)
- [Personalized News Recommendation: Methods and Challenges](https://mlsanity.com/paper/2106.08934/) (2021)
- [Dbias: Detecting biases and ensuring Fairness in news articles](https://mlsanity.com/paper/2208.05777/) (2022)
- [Digital Nudging with Recommender Systems: Survey and Future Directions](https://mlsanity.com/paper/2011.03413/) (2020)
- [PIIvot: A Lightweight NLP Anonymization Framework for Question-Anchored Tutoring Dialogues](https://mlsanity.com/paper/2505.16931/) (2025)
- [Reconciling the accuracy-diversity trade-off in recommendations](https://mlsanity.com/paper/2307.15142/) (2023)
- [Political Depolarization of News Articles Using Attribute-aware Word Embeddings](https://mlsanity.com/paper/2101.01391/) (2021)
- [Political Polarization in Online News Consumption](https://mlsanity.com/paper/2104.06481/) (2021)
- [Interactive Content Diversity and User Exploration in Online Movie Recommenders: A Field Experiment](https://mlsanity.com/paper/2309.13296/) (2023)
- [Increasing Participation in Peer Production Communities with the Newcomer Homepage](https://mlsanity.com/paper/2308.09642/) (2023)

## Abstract (arXiv)

In this study, we applied the ``personalized diversity nudge framework'' with the goal of expanding user reading coverage in terms of news locality (i.e., domestic and world news). We designed a novel topic-locality dual calibration algorithmic nudge and a large language model-based news personalization presentation nudge, then launched a 5-week real-user study with 120 U.S. news readers on the news recommendation experiment platform POPROX. With user interaction logs and survey responses, we found that algorithmic nudges can successfully increase exposure and consumption diversity, while the impact of LLM-based presentation nudges varied. User-level topic interest is a strong predictor of user clicks, while highlighting the relevance of news articles to prior read articles outperforms generic topic-based and no personalization. We also demonstrate that longitudinal exposure to calibrated news may shift readers' reading habits to value a balanced news digest from both domestic and world articles. Our results provide direction for future work on nudging for diverse consumption in news recommendation systems.