Text cohesion is an important element of discourseprocessing. This paper presents a new approach to modeling,quantifying, and visualizing text cohesion using automatedcohesion flow indices that capture semantic links amongparagraphs. Cohesion flow is calculated by applyingCohesion Network Analysis, a combination of semanticdistances, Latent Semantic Analysis, and Latent DirichletAllocation, as well as Social Network Analysis. Experimentsperformed on 315 timed essays indicated that cohesion flowindices are significantly correlated with human ratings of textcoherence and essay quality. Visualizations of the globalcohesion indices are also included to support a more facileunderstanding of how cohesion flow impacts coherence interms of semantic dependencies between paragraphs.

Dialogism provides the grounds for building a comprehensivemodel of discourse and it is focused on the multiplicity ofperspectives (i.e., voices). Dialogism can be present in anytype of text, while voices become themes or recurrent topicsemerging from the discourse. In this study, we examine theextent that differences between self-explanations and think-alouds can be detected using computational textual indicesderived from dialogism. Students (n = 68) read a text aboutnatural selection and were instructed to generate self-explanations or think-alouds. The linguistic features of thesetext responses were analyzed using ReaderBench, anautomated text analysis tool. A discriminant function analysisusing these features correctly classified 80.9% of the students’assigned experimental conditions (self-explanation vs. thinkaloud). Our results indicate that self-explanation promotestext processing that focuses on connected ideas, rather thanseparate voices or points of view covering multiple topics.