The quality of a conversation goes beyond the individual quality of each reply, and instead emerges from how these combine into interactional patterns that give the conversation its distinctive overall "shape". However, there is no robust automated method for comparing conversations in terms of their overall interactional dynamics. Such methods could enhance the analysis of conversational data and help evaluate conversational agents more holistically.

In this work, we introduce a similarity measure for comparing conversations with respect to their dynamics. We design a validation framework for testing the robustness of the metric in capturing differences in conversation dynamics and for assessing its sensitivity to the topic of the conversations. Finally, to illustrate the measure's utility, we use it to analyze conversational dynamics in a large online community, bringing new insights into the role of situational power in conversations.

@InProceedings{Jung-similarity:25,

author={Sang Min Jung, Kaixiang Zhang, Cristian Danescu-Niculescu-Mizil},

title={A Similarity Measure for Comparing Conversational Dynamics},

booktitle={Proceedings of EMNLP (Findings)},

year={2025}

}