@inproceedings{hughes-etal-2025-computational,
    title = "Computational Analysis of Conversation Dynamics through Participant Responsivity",
    author = "Hughes, Margaret  and
      Roy, Brandon  and
      Poole-Dayan, Elinor  and
      Roy, Deb  and
      Kabbara, Jad",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.emnlp-main.1798/",
    doi = "10.18653/v1/2025.emnlp-main.1798",
    pages = "35500--35519",
    ISBN = "979-8-89176-332-6",
    abstract = "Growing literature explores toxicity and polarization in discourse, with comparatively less work on characterizing what makes dialogue prosocial and constructive. We explore conversational discourse and investigate a method for characterizing its quality built upon the notion of ``responsivity''{---}whether one person{'}s conversational turn is responding to a preceding turn. We develop and evaluate methods for quantifying responsivity{---}first through semantic similarity of speaker turns, and second by leveraging state-of-the-art large language models (LLMs) to identify the relation between two speaker turns. We evaluate both methods against a ground truth set of human-annotated conversations. Furthermore, selecting the better performing LLM-based approach, we characterize the nature of the response{---}whether it responded to that preceding turn in a substantive way or not. We view these responsivity links as a fundamental aspect of dialogue but note that conversations can exhibit significantly different responsivity structures. Accordingly, we then develop conversation-level derived metrics to address various aspects of conversational discourse. We use these derived metrics to explore other conversations and show that they support meaningful characterizations and differentiations across a diverse collection of conversations."
}