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A Dynamic Dyadic Systems Perspective on Communication of Real-Time Support Between Graduate Women in STEM and Their Mentor

ABSTRACT Women of Color (WoC) in science, technology, engineering, and math (STEM) leave doctoral programs at disproportionately high rates. Supportive mentorship is key to increasing belonging and rates of retention, yet little is known about how conversations between mentees and their mentors on academic and personal stress topics unfold in real-time. Applying the lens of Social Cognitive Career Theory to communication dynamics between mentees and mentors, the present study utilized a dynamic dyadic systems (DDS) perspective to examine observationally coded data from six mentee-mentor dyads. First, hierarchical clustering analysis was applied to identify speaking turn types. Then, sequence analysis was used to identify common multi-turn patterns or conversation motifs (CM). Results showed five predominant CMs: (CM1) support provision through listening; (CM2) focus on mentor’s experience; (CM3) support provision through advice; (CM4) mentee’s making a bid for support; and (CM5) mentor dominated conversations. This study demonstrates methods for identifying potentially meaningful patterns of support in stress conversations between mentees and mentors. The application of such methods with larger samples may aid in understanding ways to increase retention among WoC in STEM through mentor support provision.

A Dynamic Dyadic Systems Perspective on Interpersonal Conversation

ABSTRACT 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 human-computer interaction, family communication patterns, health care interventions, and group deliberation.

Building an ICCN Multimodal Classifier of Aggressive Political Debate Style: Towards a Computational Understanding of Candidate Performance Over Time

ABSTRACT Understanding the implications of aggressive political debate style amid corrosive modes of campaign politics requires fine-grained analyses of political performance, attending to multiple communication modalities. Politicians’ facial expressions, emotional tone, and speech content can all independently convey aggression and dominance, and often work in combination for purposes of emphasis. Yet micro-coding individual visual, tonal, and verbal features across more than a handful of debate segments becomes extremely labor intensive, hampering research, especially historical, longitudinal, and cross-cultural work. To address this limitation, we develop a novel multimodal classifier using an Interaction Canonical Correlation Network (ICCN) that incorporates video and audio features with speech coding of candidate debate performance, trained on a 20% sample of 10-second segments from each of the first televised U.S presidential debates between 1980 and 2020. In the analysis, we demonstrate this classifier can accurately detect aggression by political candidates in U.S. debates. We sharpen its performance by distinguishing between debate eras characterized by lower and higher levels of aggression and validate the approach by comparing the performance of unimodal with multimodal classification. This classifier opens new avenues for computational social science research, including explaining candidate behavior within debates at a larger scale and across different eras.

An Analysis of Turn Transitions and Conversational Motifs in Parent-Adolescent Emotion-Focused Interactions

ABSTRACT Informed by the dynamic dyadic systems perspective on analyzing conversational data, this study uses sequence analysis to illuminate turn patterns that characterize parents’ emotion coaching/dismissing communication and adolescents’ emotion regulation/dysregulation in conversations about an emotionally evocative event. This study analyzed two conversations from 60 parent-adolescent dyads, 30 with harmful parental alcohol use and 30 without, where the adolescent described recent events that elicited positive and negative feelings. Using configural frequency analysis, we identified turn sequences that were over- and under-represented in the data, and we compared the prevalence of different turn sequences between families with harmful and non-harmful alcohol use. Then, using sequence analysis, we identified conversational motifs that reflect different patterns of parental communication and adolescent emotion regulation in conversation. Three conversational motifs emerged in conversations about negative emotions: (a) information sharing, (b) emotion regulating, and (c) reactive. Conversations about positive emotions revealed two conversational motifs: (a) emotion regulating and (b) information sharing. In addition, the conversational motifs were examined as predictors of adolescents’ post- interaction appraisals of their own emotion regulation and their parent’s responsiveness and control during the conversation. Findings point to the utility of sequence analysis for documenting patterns of interaction in parent-adolescent emotion-focused conversations.

Uncovering Hidden Media Framings in Generic Communication Competence Assessments: Is the Face-To-Face Context the Default Framing?

ABSTRACT Dispositional communication competencies can be assessed in (a) a generic form that does not include any reference to a particular medium of interaction or in (b) a communication medium-specific version. To date, little is known about the specific media that individuals use as a reference and the weights they assign to them when responding to generic communication items – an important research gap because the use of diverse communication media has risen considerably during the COVID-19 pandemic. Based on media theories, two hypotheses were derived: Generic ratings contain a “hidden” face-to-face (FtF) communication framing that is dominant in the cognitive processing (media naturalness perspective) versus media are equally weighted in the mental aggregate of respondents (adaptation perspective). According to a preregistered study plan, generic and medium-specific communication items were assessed to investigate these hypotheses (referencing FtF, videoconferencing, chat, and e-mail interaction contexts). Training (n = 200) and test (n = 389) datasets were analyzed using latent variable modeling. Results indicated that generic ratings have a strong hidden FtF framing. These hidden framings impact the predictive power of the competencies to explain communication criteria (i.e. communication satisfaction). Exploratory analyses indicated that individual differences in media experience may affect the framings.