LLMs Enhance Emotional Expression While Maintaining Analytical Depth in News Writing

Abstract

As the impact of generative artificial intelligence (GenAI) becomes increasingly evident in automated journalism, leveraging its potential while mitigating the risks becomes a priority in research. To address this need, our study evaluated the performance of large language models (LLMs) in news writing. We tested 11 LLMs by having them rewrite headlines and content from articles published by Milwaukee Neighborhood News Service (NNS) between 2011 and 2023. The analysis and comparison of 3,623 human-written and 39,853 AI-adapted news pieces showed that different LLMs consistently enhanced emotional expression in headlines (Cohen’s d = .33) and in news content (Cohen’s d = .83). Importantly, this emotional enhancement did not seem to compromise analytical thinking, while some LLMs even improved the analytical depth of reporting. The theoretical and practical implications are discussed, particularly regarding the importance of high-quality training data and how LLMs can better assist journalists in newsrooms.