  Large Language Models (LLMs) are already as persuasive as humans. However, we
know very little about how they do it. This paper investigates the persuasion
strategies of LLMs, comparing them with human-generated arguments. Using a
dataset of 1,251 participants in an experiment, we analyze the persuasion
strategies of LLM-generated and human-generated arguments using measures of
cognitive effort (lexical and grammatical complexity) and moral-emotional
language (sentiment and moral analysis). The study reveals that LLMs produce
arguments that require higher cognitive effort, exhibiting more complex
grammatical and lexical structures than human counterparts. Additionally, LLMs
demonstrate a significant propensity to engage more deeply with moral language,
utilizing both positive and negative moral foundations more frequently than
humans. In contrast with previous research, no significant difference was found
in the emotional content produced by LLMs and humans. These findings contribute
to the discourse on AI and persuasion, highlighting the dual potential of LLMs
to both enhance and undermine informational integrity through communication
strategies for digital persuasion.
