Mental health counseling is an enterprise with profound societ al importance where conversations play a primary role. In order to acquire the conversational skills needed to face a challenging range of situations, mental health counselors must rely on training and on continued experience with actual clients. However, in the absence of large scale longitudinal studies, the nature and significance of this developmental process remain unclear. For example, prior literature suggests that experience might not translate into consequential changes in counselor behavior. This has led some to even argue that counseling is a profession without expertise. In this work, we develop a computational framework to quantify the extent to which individuals change their linguistic behavior with experience and to study the nature of this evolution. We use our framework to conduct a large longitudinal study of mental health counseling conversations, tracking over 3,400 counselors across their tenure. We reveal that overall, counselors do indeed change their conversational behavior to become more diverse across interactions, developing an individual voice that distinguishes them from other counselors. Furthermore, a finer-grained investigation shows that the rate and nature of this diversification vary across functionally different conversational components.
Linguistic behavior in conversations Our work relates more broadly to other computational studies of conversations. As in the mental health setting, these studies have largely considered the behavior of interlocutors in a single conversation, tying it to outcomes such as persuasion, problem-solving and incivility @cite_9 @cite_3 @cite_13 @cite_35 , or to social correlates such as power, gender and age @cite_5 @cite_17 @cite_54 @cite_55 @cite_23 . In contrast, our work seeks to chart the development of linguistic tendencies of individuals as they engage in many conversations.
@cite_35 @cite_9 @cite_54 @cite_55 @cite_3 @cite_23 @cite_5 @cite_13 @cite_17
2963955897 2156333962 2147154374 1893888233 2251911493 2250334514 2130703925 2615257384 2126601124
In this research we examine whether conversational dynamics occurring within the first five minutes of a negotiation can predict negotiated outcomes. In a simulated employment negotiation, micro-coding conducted by a computer showed that activity level, conversational engagement, prosodic emphasis, and vocal mirroring predicted 30 of the variance in individual outcomes. The conversational dynamics associated with individual success among high-status parties were different from those associated with individual success among low-status parties. Results are interpreted in light of theory and research exploring the predictive power of "thin slices" (Ambady & Rosenthal, 1992). Implications include the development of new technology to diagnose and improve negotiation processes. Understanding social interaction within groups is key to analyzing online communities. Most current work focuses on structural properties: who talks to whom, and how such interactions form larger network structures. The interactions themselves, however, generally take place in the form of natural language --- either spoken or written --- and one could reasonably suppose that signals manifested in language might also provide information about roles, status, and other aspects of the group's dynamics. To date, however, finding domain-independent language-based signals has been a challenge. Here, we show that in group discussions, power differentials between participants are subtly revealed by how much one individual immediately echoes the linguistic style of the person they are responding to. Starting from this observation, we propose an analysis framework based on linguistic coordination that can be used to shed light on power relationships and that works consistently across multiple types of power --- including a more "static" form of power based on status differences, and a more "situational" form of power in which one individual experiences a type of dependence on another. Using this framework, we study how conversational behavior can reveal power relationships in two very different settings: discussions among Wikipedians and arguments before the U. S. Supreme Court. In this paper we focus on the connection between age and language use, exploring age prediction of Twitter users based on their tweets. We discuss the construction of a fine-grained annotation effort to assign ages and life stages to Twitter users. Using this dataset, we explore age prediction in three different ways: classifying users into age categories, by life stages, and predicting their exact age. We find that an automatic system achieves better performance than humans on these tasks and that both humans and the automatic systems have difficulties predicting the age of older people. Moreover, we present a detailed analysis of variables that change with age. We find strong patterns of change, and that most changes occur at young ages. Determining when conversational participants agree or disagree is instrumental for broader conversational analysis; it is necessary, for example, in deciding when a group has reached consensus. In this paper, we describe three main contributions. We show how different aspects of conversational structure can be used to detect agreement and disagreement in discussion forums. In particular, we exploit information about meta-thread structure and accommodation between participants. Second, we demonstrate the impact of the features using 3-way classification, including sentences expressing disagreement, agreement or neither. Finally, we show how to use a naturally occurring data set with labels derived from the sides that participants choose in debates on createdebate.com. The resulting new agreement corpus, Agreement by Create Debaters (ABCD) is 25 times larger than any prior corpus. We demonstrate that using this data enables us to outperform the same system trained on prior existing in-domain smaller annotated datasets. We investigate the interaction of power, gender, and language use in the Enron email corpus. We present a freely available extension to the Enron corpus, with the gender of senders of 87 messages reliably identified. Using this data, we test two specific hypotheses drawn from the sociolinguistic literature pertaining to gender and power: women managers use face-saving communicative strategies, and women use language more explicitly than men to create and maintain social relations. We introduce the notion of “gender environment” to the computational study of written conversations; we interpret this notion as the gender makeup of an email thread, and show that some manifestations of power differ significantly between gender environments. Finally, we show the utility of gender information in the problem of automatically predicting the direction of power between pairs of participants in email interactions. Synchronized verbal behavior can reveal important information about social dynamics. This study introduces the linguistic style matching (LSM) algorithm for calculating verbal mimicry based on an automated textual analysis of function words. The LSM algorithm was applied to language generated during a small group discussion in which 70 groups comprised of 324 individuals engaged in an information search task either face-to-face or via text-based computer-mediated communication. As a metric, LSM predicted the cohesiveness of groups in both communication environments, and it predicted task performance in face-to-face groups. Other language features were also related to the groups’ cohesiveness and performance, including word count, pronoun patterns, and verb tense. The results reveal that this type of automated measure of verbal mimicry can be an objective, efficient, and unobtrusive tool for predicting underlying social dynamics. In total, the study demonstrates the effectiveness of using language to predi... Debate and deliberation play essential roles in politics and government, but most models presume that debates are won mainly via superior style or agenda control. Ideally, however, debates would be won on the merits, as a function of which side has the stronger arguments. We propose a predictive model of debate that estimates the effects of linguistic features and the latent persuasive strengths of different topics, as well as the interactions between the two. Using a dataset of 118 Oxford-style debates, our model's combination of content (as latent topics) and style (as linguistic features) allows us to predict audience-adjudicated winners with 74 accuracy, significantly outperforming linguistic features alone (66 ). Our model finds that winning sides employ stronger arguments, and allows us to identify the linguistic features associated with strong or weak arguments. Previous relationship research has largely ignored the importance of similarity in how people talk with one another. Using natural language samples, we investigated whether similarity in dyads’ use of function words, called language style matching (LSM), predicts outcomes for romantic relationships. In Study 1, greater LSM in transcripts of 40 speed dates predicted increased likelihood of mutual romantic interest (odds ratio = 3.05). Overall, 33.3 of pairs with LSM above the median mutually desired future contact, compared with 9.1 of pairs with LSM at or below the median. In Study 2, LSM in 86 couples’ instant messages positively predicted relationship stability at a 3-month follow-up (odds ratio = 1.95). Specifically, 76.7 of couples with LSM greater than the median were still dating at the follow-up, compared with 53.5 of couples with LSM at or below the median. LSM appears to reflect implicit interpersonal processes central to romantic relationships.