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1. A Dynamic Dyadic Systems Perspective on Interpersonal Conversation

   https://doi.org/10.1080/19312458.2023.2237404  

   Solomon, Denise Haunani; Brinberg, Miriam; Bodie, Graham; Jones, Susanne; Ram, Nilam (October 2023, Communication Methods and Measures)

   Solomon, Denise Haunani; Brinberg, Miriam; Bodie, Graham; Jones, Susanne; Ram, Nilam (Ed.)

   Conversations between people are where, among other things, stressors are amplified and attenuated, conflicts are entrenched and resolved, and goals are advanced and thwarted. What happens in dyads’ back-and-forth exchanges to produce such consequential and varied outcomes? Although numerous theories in communication and in social psychology address this question, empirical tests of these theories often operationalize conversational behavior using either discrete messages or overall features of the conversation. Dynamic systems theories and methods provide opportunities to examine the interdependency, self-stabilization, and self-organization processes that manifest in conversations over time. The dynamic dyadic systems perspective exemplified by the articles in this special issue (a) focuses inquiry on the turn-to-turn, asynchronous exchange of messages between two partners, (b) emphasizes behavioral patterns within and the structural and temporal organization of conversations, and (c) adapts techniques used in analysis of intensive longitudinal data to identify and operationalize those dynamic patterns. As an introduction to the special issue, this paper describes a dynamic dyadic systems perspective on conversation and discusses directions for future research, such as applications to humancomputer interaction, family communication patterns, health care interventions, and group deliberation. 

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2. Using Sequence Analysis to Identify Conversational Motifs in Supportive Interactions

   https://doi.org/10.1177/02654075211066618  

   Solomon, Denise Haunani; Jones, Susanne; Brinberg, Miriam; Bodie, Graham D.; Ram, Nilam (January 2022, Journal of Social and Personal Relationships)

   This study demonstrates how sequence analysis, which is a method for identifying common patterns in categorical time series data, illuminates the nonlinear dynamics of dyadic conversations by describing chains of behavior that shift categorically, rather than incrementally. When applied to interpersonal interactions, sequence analysis supports the identification of conversational motifs, which can be used to test hypotheses linking patterns of interaction to conversational antecedents or outcomes. As an illustrative example, this study evaluated 285 conversations involving stranger, friend, and dating dyads in which one partner, the discloser, communicated about a source of stress to a partner in the role of listener. Using sequence analysis, we identified three five-turn supportive conversational motifs that had also emerged in a previous study of stranger dyads: discloser problem description, discloser problem processing, and listener-focused dialogue. We also observed a new, fourth motif: listener-focused, discloser questioning. Tests of hypotheses linking the prevalence and timing of particular motifs to the problem discloser’s emotional improvement and perceptions of support quality, as moderated by the discloser’s pre-interaction stress, offered a partial replication of previous findings. The discussion highlights the value of using sequence analysis to illuminate dynamic patterns in dyadic interactions. 

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3. Discovering the Fabric of Supportive Conversations: A Typology of Speaking Turns and Their Contingencies

   https://doi.org/10.1177/0261927X20953604  

   Bodie, Graham D.; Jones, Susanne M.; Brinberg, Miriam; Joyer, Amy M.; Solomon, Denise Haunani; Ram, Nilam (March 2021, Journal of Language and Social Psychology)

   null (Ed.)

   This study examines messages that problem disclosers and supportive listeners enact during conversations about everyday stressors. We coded 402 dyadic interactions between strangers, friends, and romantic couples using Stiles’s (1992) verbal response modes (VRM) and Burleson’s (1982) verbal person centeredness (PC) typology to explore whether (a) listener and discloser utterances coalesce into types of speaking turns, (b) listener turn types vary in person-centered quality, (c) listener turns relate to discloser responses, and (d) discloser responses relate to listener turns. Analyses revealed a typology for both listener and discloser turns: acknowledgment, advisement, question, elaboration, hedged disclosure, and reflection. The relative proportion of those types varied as a function of conversational role and relationship context, and these speech acts varied only minimally in PC. Configural frequency analyses revealed four greater-than-chance contingencies across data sets. The discussion highlights implications for a dyadic and dynamic understanding of supportive communication. 

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4. Feedstack: Layering Structured Representations Over Unstructured Feedback to Scaffold Human–AI Conversation

   https://doi.org/10.1145/3719160.3737636  

   Nguyen, Hannah Vy; Yen, Grace Yu-Chun; Shakir, Omar; Huynh, Hang; Gutierrez, Sebastian; Smith, June A; Jimenez, Sheila; Abdelgelil, Salma E; MacNeil, Stephen (July 2025, ACM)

   Not AvailableMany conversational user interfaces facilitate linear conversations with turn-based dialogue, similar to face-to-face conversations between people. However, digital conversations can afford more than simple back-and-forth; they can be layered with interaction techniques and structured representations that scaffold exploration, reflection, and shared understanding between users and AI systems. We introduce Feedstack, a speculative interface that augments feedback conversations with layered affordances for organizing, navigating, and externalizing feedback. These layered structures serve as a shared representation of the conversation that can surface user intent and reveal underlying design principles. This work represents an early exploration of this vision using a research-through-design approach. We describe system features and design rationale, and present insights from two formative (n=8, n=8) studies to examine how novice designers engage with these layered supports. Rather than presenting a conclusive evaluation, we reflect on Feedstack as a design probe that opens up new directions for conversational feedback systems. 

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5. Trimodal prediction of speaking and listening willingness to help improve turn-changing modeling

   https://doi.org/10.3389/fpsyg.2022.774547  

   Ishii, Ryo; Ren, Xutong; Muszynski, Michal; Morency, Louis-Philippe (October 2022, Frontiers in Psychology)

   Participants in a conversation must carefully monitor the turn-management (speaking and listening) willingness of other conversational partners and adjust their turn-changing behaviors accordingly to have smooth conversation. Many studies have focused on developing actual turn-changing (i.e., next speaker or end-of-turn) models that can predict whether turn-keeping or turn-changing will occur. Participants' verbal and non-verbal behaviors have been used as input features for predictive models. To the best of our knowledge, these studies only model the relationship between participant behavior and turn-changing. Thus, there is no model that takes into account participants' willingness to acquire a turn (turn-management willingness). In this paper, we address the challenge of building such models to predict the willingness of both speakers and listeners. Firstly, we find that dissonance exists between willingness and actual turn-changing. Secondly, we propose predictive models that are based on trimodal inputs, including acoustic, linguistic, and visual cues distilled from conversations. Additionally, we study the impact of modeling willingness to help improve the task of turn-changing prediction. To do so, we introduce a dyadic conversation corpus with annotated scores of speaker/listener turn-management willingness. Our results show that using all three modalities (i.e., acoustic, linguistic, and visual cues) of the speaker and listener is critically important for predicting turn-management willingness. Furthermore, explicitly adding willingness as a prediction task improves the performance of turn-changing prediction. Moreover, turn-management willingness prediction becomes more accurate when this joint prediction of turn-management willingness and turn-changing is performed by using multi-task learning techniques. 

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