Emotional Reframing of Economic News using a Large Language Model

News media framing can shape public perception and potentially polarize views. Emotional language can exacerbate these framing effects, as a user's emotional state can be an important contextual factor to use in news recommendation. Our research explores the relation between emotional framing techniques and the emotional states of readers, as well as readers' perceived trust in specific news articles. Users (N = 200) had to read three economic news articles from the Washington Post. We used ChatGPT-4 to reframe news articles with specific emotional languages (Anger, Fear, Hope), compared to a neutral baseline reframed by a human journalist. Our results revealed that negative framing (Anger, Fear) elicited stronger negative emotional states among users than the neutral baseline, while Hope led to little changes overall. In contrast, perceived trust levels varied little across the different conditions. We discuss the implications of our findings and how emotional framing could affect societal polarization issues.