Leveraging Media Frames to Improve Normative Diversity in News Recommendations

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.