Using large language models to ‘lighten the mood’ Satirically reframing news recommendations to reduce news avoidance

News avoidance is a growing issue that leads to less informed citizens and endangers democratic processes. This also poses problems in news recommender environments, as’unpleasant’ news content could be avoided through personalized algorithms. To ‘lighten the user’s mood’, this paper investigates whether satirical re-framing of news article summaries, generated by Large Language Models (LLMs), can mitigate news avoidance by making news content more engaging. Through two online experiments (N = 89; N = 151), we tested various prompting techniques, assessing the impact on user perception, humor, understanding, and news consumption choices. Results indicate that satirically re-framed summaries were perceived to be engaging and informative. Less frequent news consumers showed a stronger preference for satirical content, suggesting that satire could be a tool for reconnecting with disengaged audiences. These findings show the promise of AI-generated personalized satire as an innovative approach to reducing news avoidance.