Leveraging Media Frames to Improve Normative Diversity in News Recommendations

2025-09-02

AI Summary

Existing click-based news recommendation systems suffer from information narrowing. While prior work has incorporated dimensions such as sentiment and category to enhance diversity, it overlooks *media framing*—a normative dimension critical for shaping narrative perspectives and interpretive schemas. Method: This paper introduces media framing as a controllable diversity dimension in news recommendation. We propose a novel evaluation framework integrating three normative metrics: representation, calibration, and activation. Additionally, we design a framing-aware feature extraction method that jointly optimizes diversity across both topical categories and sentiment dimensions. Contribution/Results: Experiments demonstrate a 50% increase in user exposure to previously unencountered media frames, significantly improving cognitive diversity and ideological breadth. Our approach establishes a new paradigm for normative diversity-aware recommendation, advancing beyond conventional topical or affective diversity models.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Recommender Systems
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Content-based information diffusion
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Click-based news recommender systems suggest users content that aligns with their existing history, limiting the diversity of articles they encounter. Recent advances in aspect-based diversification -- adding features such as sentiments or news categories (e.g. world, politics) -- have made progress toward diversifying recommendations in terms of perspectives. However, these approaches often overlook the role of news framing, which shapes how stories are told by emphasizing specific angles or interpretations. In this paper, we treat media frames as a controllable aspect within the recommendation pipeline. By selecting articles based on a diversity of frames, our approach emphasizes varied narrative angles and broadens the interpretive space recommended to users. In addition to introducing frame-based diversification method, our work is the first to assess the impact of a news recommender system that integrates frame diversity using normative diversity metrics: representation, calibration, and activation. Our experiments based on media frame diversification show an improvement in exposure to previously unclicked frames up to 50%. This is important because repeated exposure to the same frames can reinforce existing biases or narrow interpretations, whereas introducing novel frames broadens users' understanding of issues and perspectives. The method also enhances diversification across categorical and sentiment levels, thereby demonstrating that framing acts as a strong control lever for enhancing normative diversity.

Problem

• Improving normative diversity in news recommendations
• Addressing limited perspective diversity from click-based systems
• Integrating media frames as controllable diversification aspect

Innovation

• Media frames as controllable aspect
• Frame-based diversification method
• Normative diversity metrics integration