LLMs Can Amplify Emotion While Preserving Analytic Style in AI-mediated Communication
TL;DR
GenAI and LLMs have the potential to enhance the affective intensity of communication without necessarily compromising its informational quality, however, as human communication becomes increasingly and inevitably AI-mediated, current LLMs may shift the affective register, narrow the emotional spectrum, and introduce tonal biases in public communication.
Abstract
The ubiquity of generative AI (GenAI) requires social computing scholarship to examine how such extensive AI mediation may (re)shape the emotional and analytic qualities of human communication, both of which are central to audience engagement, trust, and information integrity. This study conducts a large-scale evaluation of 11 custom and fine-tuned LLMs, including DeepSeek, Llama (Meta), Mistral, and Gemma (Google), by asking them to rewrite the complete set of content, published by a local news outlet over a 12-year span (2011–2023). Using transformer-based sentiment models (i.e., RoBERTa fine-tuned on GoEmotions) and lexicon-based measures (i.e., LIWC, NRCLex), we compare the emotional expressiveness and analytic style of human-written versus AI-adapted content across short-form (titles/headlines) and long-form communication (article bodies/content). Analyzing 3,623 original news articles, nearly 40,000 primary-prompt AI-generated rewrites, and an additional generic-prompt ablation set, we estimate linear mixed-effects models that account for article-level clustering and control for model and prompt heterogeneity, communication form, verbosity, and readability. Results show that, under the corpus and prompting conditions examined here, LLMs tend to intensify emotional expression relative to human-written texts. Meanwhile, LLM rewrites tend to preserve, and in many cases increase, linguistic markers associated with analytic style relative to human benchmarks. Interestingly, even under the simpler generic prompt, LLMs in our sample display a shift toward more positive emotional categories, including joy, excitement, admiration, optimism, and gratitude, whereas human writers in this corpus display a broader affective range and more nuanced expression. To assess risks of clickbait-like headline framing, we fine-tune a BERT model for clickbait detection and examine linguistic cues in titles and headlines, yet we find minimal evidence that LLMs’ emotional amplification devolves into sensational and hyperbolic framing. Lastly, to enhance validity, we conduct a blinded human evaluation study on a randomly sampled subset of the corpus. Across 17 small experiments, human judges (n = 170) independently evaluated analytic style, reasoning depth, and clickbait characteristics. These human evaluations provide convergent evidence that AI-generated texts were perceived as more analytically structured and slightly higher in perceived reasoning depth, while AI-generated headlines were also rated as less clickbait-oriented. Overall, our findings suggest that GenAI and LLMs have the potential to enhance the affective intensity of communication without necessarily compromising its informational quality. However, as human communication becomes increasingly and inevitably AI-mediated, current LLMs may shift the affective register, narrow the emotional spectrum, and introduce tonal biases in public communication that could subtly influence how information is disseminated, and how language is traditionally interpreted.