A Computational Study of Persuasion in Dialogue: Linguistic Features and Conversational Context

Abstract

This work analyzed the relationship between persuasive language and its observable effect, agreement, in structured dyadic dialogue. In particular, we studied the presence and distribution of linguistic features and Cialdini-inspired persuasion techniques regarding conversational behavior. We leveraged large language models (LLMs) to annotate for persuasion principles across multiple dialogue datasets, while also examining the reliability and consistency of LLM-based evaluation. We analyzed these datasets using a combination of Ordinary Least Squares (OLS) linear regression modeling, Multivariate Analyses of Variance (MANOVAs), K-Means clustering, and descriptive comparisons to evaluate whether these features were indicative of conversational outcomes and contextual conditions, specifically in the form of pre-conversational prompting. Our findings showed that while these features provided limited predictive power for agreement outcomes, the discourse markers, structural properties, and persuasion principles varied across different prompting conditions: persuasion, compromise, and general dyadic dialogue. These results suggest that conversational intent played a significant role in shaping feature usage, while also emphasizing the importance of context in computational analyses of dialogue.