Modeling Language Characteristics of Leaders in Authoritarian Regimes over Decades

The present research investigated the linguistic patterns in the discourse of three prominent autocratic political leaders whose reigns lasted for multiple decades. The texts of Fidel Castro, Zedong Mao, and Hosni Mubarak were analyzed using computational linguistic methodologies and nonlinear modeling techniques to explore the temporal trajectory of formality over time. Specifically, this metric of formality increases with abstractness of words, syntactic complexity, cohesion (referential and deep), and the informational genre (as opposed to narrative). At the other end of the continuum, informal discourse tends to have concrete words, simple syntax, low cohesion and high narrativity. The findings are aligned with theoretically grounded hypotheses of aging and persuasion in hopes of identifying which most appropriately explains the formality of leaders’ political texts

A Computational Linguistic Analysis of Learners Discourse in Computer-Mediated Group Learning Environments

Communication, collaboration and the social co-construction of knowledge are now considered critical 21st century skills and have taken a principal role in recent theoretical and technological developments in education research. The overall objective of this dissertation was to investigate collaborative learning to gain insight on why some groups are more successful than others. In such discussions, group members naturally assume different roles. These roles emerge through participants’ interactions without any prior instruction or assignment. Different combinations of these roles can produce characteristically different group outcomes, being either less or more productive towards collective goals. However, there has been little research on how to automatically identify these roles and fuse the quality of the process of collaborative interactions with the learning outcome. A major goal of this dissertation is to develop a group communication analysis (GCA) framework, a novel methodology that applies automated computational linguistic techniques to the sequential interactions of online group communication. The GCA involves computing six distinct measures of participant discourse interaction and behavioral patterns and then clustering participants based on their profiles across these measures. The GCA was applied to several large collaborative learning datasets, and identified roles that exhibit distinct patterns in behavioral engagement style (i.e., active or passive, leading or following), contribution characteristics (i.e., providing new information or echoing given material), and social orientation. Through bootstrapping and replication analysis, the roles were found to generalize both within and across different collaborative interaction datasets, indicating that these roles are robust constructs. A multilevel analysis shows that the social roles are predictive of success, both for individual team members and for the overall group. Furthermore, the presence of specific roles within a team produce characteristically different outcomes; leading to specific hypotheses as to optimal group composition. Ideally, the developed analytical tools and findings of this dissertation will contribute to our understanding of how individuals learn together as a group and thereby advance the learning and discourse sciences. More broadly, GCA provides a framework to explore the intra- and inter-personal patterns indicative of the participants’ roles and the sociocognitive processes related to successful collaboration

Leader Language and Political Survival Strategies

Authoritarian leaders’ language provides clues to their survival strategies for remaining in office. This line of inquiry fits within an emerging literature that refocuses attention from state-level features to the dynamic role that individual heads of state and government play in international relations, especially in authoritarian regimes. The burgeoning text-as-data field can be used to deepen our understanding of the nuances of leader survival and political choices; for example, language can serve as a leading indicator of leader approval, which itself is a good predictor of leader survival. In this paper, we apply computational linguistics tools to an authoritarian leader corpus consisting of 102 speeches from nine leaders of countries across the Middle East and North Africa between 2009 and 2012. We find systematic differences in the language of these leaders, which help advance a more broadly applicable theory of authoritarian leader language and tenure

Therapist nonverbal behavior and perceptions of empathy, alliance, and treatment credibility

The aim of this study was to examine the potential independent and joint impact of 2 specific therapist nonverbal behaviors-eye contact and trunk lean-on perceptions of therapist empathy, the relationship between client and therapist, and the credibility of the treatment. Four different psychotherapists were filmed in 4 combinations of eye contact and trunk lean. Participants rated these therapists after viewing a randomized order of the therapy session videos. Findings indicate that high eye contact and forward trunk lean enhanced perceived therapist empathy, therapeutic alliance, and treatment credibility. These results suggest that therapists could improve their practice by using specific nonverbal behaviors. © 2013 American Psychological Association

Leader Language and Political Survival Strategies

Authoritarian leaders’ language provides clues to their survival strategies for remaining in office. This line of inquiry fits within an emerging literature that refocuses attention from state-level features to the dynamic role that individual heads of state and government play in international relations, especially in authoritarian regimes. The burgeoning text-as-data field can be used to deepen our understanding of the nuances of leader survival and political choices; for example, language can serve as a leading indicator of leader approval, which itself is a good predictor of leader survival. In this paper, we apply computational linguistics tools to an authoritarian leader corpus consisting of 102 speeches from nine leaders of countries across the Middle East and North Africa between 2009 and 2012. We find systematic differences in the language of these leaders, which help advance a more broadly applicable theory of authoritarian leader language and tenure

Impact of argumentation scripts on socio-cognitive conflict induction in intelligent tutoring system environments

Socio-cognitive conflict is an internal contradictory state that the individual perceives in the social interaction. Previously, researchers have successfully proved that the socio-cognitive conflict had an active effect for learning by presenting contradictory information scrips in group learning environments, such as small group discussion or computer-mediated collaborative problem solving. However, the impact of argumentation scripts on group learning performance has not been investigated to the same degree, although the argumentation scripts foster critical and reflective activities in group learning by restricting the set of communicative possibilities. In this chapter, we first examine the internal mechanism of socio-cognitive conflict on learning gains, and then explore the relationship between socio-cognitive conflict and argumentation scripts in college students within a simulated group learning session in which the human learner and two virtual peer agents work. Findings show that confusion partially mediated the relationship between socio-cognitive conflict and learning gains, and argumentation scripts affect the impact of socio-cognitive conflict on learning gains