@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://aclanthology.org/2025.emnlp-main.1798/",
doi = "10.18653/v1/2025.emnlp-main.1798",
pages = "35512--35531",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Computational Analysis of Conversation Dynamics through Participant Responsivity
%A Hughes, Margaret
%A Roy, Brandon
%A Poole-Dayan, Elinor
%A Roy, Deb
%A Kabbara, Jad
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F hughes-etal-2025-computational
%X 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.
%R 10.18653/v1/2025.emnlp-main.1798
%U https://aclanthology.org/2025.emnlp-main.1798/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1798
%P 35512-35531
Markdown (Informal)
[Computational Analysis of Conversation Dynamics through Participant Responsivity](https://aclanthology.org/2025.emnlp-main.1798/) (Hughes et al., EMNLP 2025)
ACL