Large Language Models are as persuasive as humans, but why? About the
  cognitive effort and moral-emotional language of LLM arguments
Carlos Carrasco-Farre
Large Language Models (LLMs) are already as persuasive as humans. However, we
know very little about why. 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 persuaion 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.