Computational Analysis of Conversation Dynamics through Participant Responsivity

• URL: http://arxiv.org/abs/2509.16464v1
• Date: Fri, 19 Sep 2025 23:13:13 GMT
• Title: Computational Analysis of Conversation Dynamics through Participant Responsivity
• Authors: Margaret Hughes, Brandon Roy, Elinor Poole-Dayan, Deb Roy, Jad Kabbara,
• Abstract summary: We develop and evaluate methods for quantifying responsivity.<n>We evaluate both methods against a ground truth set of human-annotated conversations.<n>We then develop conversation-level derived metrics to address various aspects of conversational discourse.
• Score: 18.116125865284666
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• A Similarity Measure for Comparing Conversational Dynamics [5.0205808005027075]
  There is no robust automated method for comparing conversations in terms of their overall dynamics.<n>We introduce a similarity measure for comparing conversations with respect to their dynamics.<n>We use it to analyze conversational dynamics in a large online community.
  arXiv  Detail & Related papers  (2025-07-25T04:51:11Z)
• Aligning Spoken Dialogue Models from User Interactions [55.192134724622235]
  We propose a novel preference alignment framework to improve spoken dialogue models on realtime conversations from user interactions.<n>We create a dataset of more than 150,000 preference pairs from raw multi-turn speech conversations annotated with AI feedback.<n>Our findings shed light on the importance of a well-calibrated balance among various dynamics, crucial for natural real-time speech dialogue systems.
  arXiv  Detail & Related papers  (2025-06-26T16:45:20Z)
• A Multi-view Discourse Framework for Integrating Semantic and Syntactic Features in Dialog Agents [0.0]
  Multiturn dialogue models aim to generate human-like responses by leveraging conversational context.<n>Existing methods often neglect the interactions between these utterances or treat all of them as equally significant.<n>This paper introduces a discourse-aware framework for response selection in retrieval-based dialogue systems.
  arXiv  Detail & Related papers  (2025-04-12T04:22:18Z)
• Revisiting Conversation Discourse for Dialogue Disentanglement [88.3386821205896]
  We propose enhancing dialogue disentanglement by taking full advantage of the dialogue discourse characteristics. We develop a structure-aware framework to integrate the rich structural features for better modeling the conversational semantic context. Our work has great potential to facilitate broader multi-party multi-thread dialogue applications.
  arXiv  Detail & Related papers  (2023-06-06T19:17:47Z)
• deep learning of segment-level feature representation for speech emotion recognition in conversations [9.432208348863336]
  We propose a conversational speech emotion recognition method to deal with capturing attentive contextual dependency and speaker-sensitive interactions. First, we use a pretrained VGGish model to extract segment-based audio representation in individual utterances. Second, an attentive bi-directional recurrent unit (GRU) models contextual-sensitive information and explores intra- and inter-speaker dependencies jointly.
  arXiv  Detail & Related papers  (2023-02-05T16:15:46Z)
• A Speaker-aware Parallel Hierarchical Attentive Encoder-Decoder Model for Multi-turn Dialogue Generation [13.820298189734686]
  This paper presents a novel open-domain dialogue generation model emphasizing the differentiation of speakers in multi-turn conversations. Our empirical results show that PHAED outperforms the state-of-the-art in both automatic and human evaluations.
  arXiv  Detail & Related papers  (2021-10-13T16:08:29Z)
• Who Responded to Whom: The Joint Effects of Latent Topics and Discourse in Conversation Structure [53.77234444565652]
  We identify the responding relations in the conversation discourse, which link response utterances to their initiations. We propose a model to learn latent topics and discourse in word distributions, and predict pairwise initiation-response links. Experimental results on both English and Chinese conversations show that our model significantly outperforms the previous state of the arts.
  arXiv  Detail & Related papers  (2021-04-17T17:46:00Z)
• Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue [76.88174667929665]
  A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles. In the existing retrieval-based multi-turn dialogue modeling, the pre-trained language models (PrLMs) as encoder represent the dialogues coarsely. We propose a novel model to fill such a gap by modeling the effective utterance-aware and speaker-aware representations entailed in a dialogue history.
  arXiv  Detail & Related papers  (2020-09-14T15:07:19Z)
• Dialogue-Based Relation Extraction [53.2896545819799]
  We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE. We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks. Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings.
  arXiv  Detail & Related papers  (2020-04-17T03:51:57Z)
• You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
  The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
  arXiv  Detail & Related papers  (2020-04-11T12:51:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.