Validating computer-generated measures of linguistic style matching and accommodation in patient-clinician communication

OBJECTIVE

To explore the validity of computer-analyzed linguistic style matching (LSM) in patient-clinician communication.

METHODS

Using 330 transcribed HIV patient encounters, we quantified word use with Linguistic Inquiry and Word Count (LIWC), a dictionary-based text analysis software. We measured LSM by calculating the degree to which clinicians matched patients in the use of LIWC "function words" (e.g., articles, pronouns). We tested associations of different LSM metrics with patients' perceptions that their clinicians spoke similiarly to them.

RESULTS

We developed 3 measures of LSM: 1) at the whole-visit level; (2) at the turn-by-turn level; and (3) using a "rolling-window" approach, measuring matching between clusters of 8 turns per conversant. None of these measures was associated with patient-rated speech similarity. However, we found that increasing trajectories of LSM, from beginning to end of the visit, were associated with higher patient-rated speech similarity (β 0.35, CI 0.06, 0.64), compared to unchanging trajectories.

CONCLUSIONS

Our findings point to the potential value of clinicians' adapting their communication style to match their patients, over the course of the visit.

PRACTICE IMPLICATIONS

With further validation, computer-based linguistic analyses may prove an efficient tool for generating data on communication patterns and providing feedback to clinicians in real time.

Capturing the Temporal Dynamics of Language Style Matching in Groups and Teams

This article presents a dynamic conceptualization for the assessment of language style matching (LSM) over time. LSM is a team’s mutual adaption of function words like pronouns, articles, or prepositions. LSM is a nonconsciously but frequently occurring communication behavior allowing researchers unobtrusive insights into teams’ internal dynamics. Building on guidelines for the alignment of construct and measurement, a dynamic conceptualization and method for LSM are introduced. Simulated examples and interactions of N = 160 individuals in 26 teams indicate that dynamic LSM allows for a truer estimation of LSM than the hitherto used static method. Implications for future application are discussed.

Internet Rumors During the COVID-19 Pandemic: Dynamics of Topics and Public Psychologies

The capturing of social opinions, especially rumors, is a crucial issue in digital public health. With the outbreak of the COVID-19 pandemic, the discussions of related topics have increased exponentially in social media, with a large number of rumors on the Internet, which highly impede the harmony and sustainable development of society. As human health has never suffered a threat of this magnitude since the Internet era, past studies have lacked in-depth analysis of rumors regarding such a globally sweeping pandemic. This text-based analysis explores the dynamic features of Internet rumors during the COVID-19 pandemic considering the progress of the pandemic as time-series. Specifically, a Latent Dirichlet Allocation (LDA) model is used to extract rumor topics that spread widely during the pandemic, and the extracted six rumor topics, i.e., "Human Immunity," "Technology R&D," "Virus Protection," "People's Livelihood," "Virus Spreading," and "Psychosomatic Health" are found to show a certain degree of concentrated distribution at different stages of the pandemic. Linguistic Inquiry and Word Count (LIWC) is used to statistically test the psychosocial dynamics reflected in the rumor texts, and the results show differences in psychosocial characteristics of rumors at different stages of the pandemic progression. There are also differences in the indicators of psychosocial characteristics between truth and disinformation. Our results reveal which topics of rumors and which psychosocial characteristics are more likely to spread at each stage of progress of the pandemic. The findings contribute to a comprehensive understanding of the changing public opinions and psychological dynamics during the pandemic, and also provide reference for public opinion responses to major public health emergencies that may arise in the future.

Gaining insights into organizational communication dynamics through the analysis of implicit and explicit communication

This report in the journal Gruppe. Interaktion. Organisation. Zeitschrift für Angewandte Organisationspsychologie aims at presenting how the analysis of implicit and explicit communication in organizational interaction can advance our insights into and implications for these interactions for research and science. Communication is a central process in modern organizations. Especially recurring forms of interaction in organizations (e.g., meetings or appraisal interviews) are of great importance for personal and organizational success. In these interactions, the communication between the interacting organizational members has a decisive impact on the interactions’ course and outcomes (e.g., satisfaction with the interaction, performance during the interaction). Therefore, the aim of this paper is to present two aspects of communication that are empirically shown to contribute to successful outcomes of organizational interactions. Based on a practical problem, we illustrate the analysis and implications of (1) implicit communication (that is, the use and coordination of unconsciously used function words such as pronouns, articles, or prepositions) and (2) explicit communication (that is, the overarching meaning of a statement). To further illustrate the practical relevance of both communication behaviors, we present empirical insights and their implications for practice. Taking a glance at the future, possible combinations of these communication behaviors, the resulting avenues for future research, and the importance of a strengthened cooperation between research and practice to gain more naturalistic insights into organizational communication dynamics are discussed.

Natural Language Analysis and the Psychology of Verbal Behavior: The Past, Present, and Future States of the Field

Throughout history, scholars and laypeople alike have believed that our words contain subtle clues about what we are like as people, psychologically speaking. However, the ways in which language has been used to infer psychological processes has seen dramatic shifts over time and, with modern computational technologies and digital data sources, we are on the verge of a massive revolution in language analysis research. In this article, we discuss the past and current states of research at the intersection of language analysis and psychology, summarizing the central successes and shortcomings of psychological text analysis to date. We additionally outline and discuss a critical need for language analysis practitioners in the social sciences to expand their view of verbal behavior. Lastly, we discuss the trajectory of interdisciplinary research on language and the challenges of integrating analysis methods across paradigms, recommending promising future directions for the field along the way.

Beyond Consistency: Contextual Dependency of Language Style in Monolog and Conversation

Language is highly dynamic: It unfolds over time, and we can use it to achieve a wide variety of communicative goals, from telling a story to trying to persuade another person. One aspect of language that has gained increasing popularity among researchers in the last several decades is the individual language style (LS) represented by an individual's use of function words (e.g., pronouns, articles). Previous approaches to LS mostly focus on LS of one individual in isolation, paying less attention to the fact that language emerges from interaction with others. The aim of this paper is twofold: First, we integrate LS into a dynamical theoretical framework and present an innovative methodological approach. Second, this paper aims to address how interactive conversation-as an aspect of the communicative setting-changes an individual's LS. We use recurrence quantification analysis to look at structure in patterns of LS in monologs and conversations of 118 participants. Our results showed that LS significantly differs from monolog to conversation, and post hoc analyses further revealed that the change in LS is greater for conflict than for friendly conversations. The difference between monologs and conversations is reflected more strongly in the dynamics (i.e., structure and complexity) of LS than the proportion of function words used. Theoretical implications and directions for future studies are discussed.