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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.

The broad use of computer-supported collaborative-learning (CSCL) environments (e.g., instant messenger-chats, forums, blogs in online communities, and massive open online courses) calls for automated tools to support tutors in the time-consuming process of analyzing collaborative conversations. In this article, the authors propose and validate the cohesion network analysis (CNA) model, housed within the ReaderBench platform. CNA, grounded in theories of cohesion, dialogism, and polyphony, is similar to social network analysis (SNA), but it also considers text content and discourse structure and, uniquely, uses automated cohesion indices to generate the underlying discourse representation. Thus, CNA enhances the power of SNA by explicitly considering semantic cohesion while modeling interactions between participants. The primary purpose of this article is to describe CNA analysis and to provide a proof of concept, by using ten chat conversations in which multiple participants debated the advantages of CSCL technologies. Each participant’s contributions were human-scored on the basis of their relevance in terms of covering the central concepts of the conversation. SNA metrics, applied to the CNA sociogram, were then used to assess the quality of each member’s degree of participation. The results revealed that the CNA indices were strongly correlated to the human evaluations of the conversations. Furthermore, a stepwise regression analysis indicated that the CNA indices collectively predicted 54% of the variance in the human ratings of participation. The results provide promising support for the use of automated computational assessments of collaborative participation and of individuals’ degrees of active involvement in CSCL environments. [ABSTRACT FROM AUTHOR]

Natural language interfaces and, in general, Natural Language Processing (NLP) state of the art applications cannot handle satisfactory (if they can at all) conversational implicatures, illocutionary force, stylization, parody, inner disguised polemic, disguised dialogue and other similar phenomena. Considering the ventriloquism perspective in the context of the dialogic-polyphonic theories of Mikhael Bakhtin may provide a basis on trying to cope with such phenomena. The paper introduces some basic ideas of Bakhtin related to ventriloquism in an extended sense, discusses the approaches based on ventriloquism in other domains than NLP, then it analyzes several NLP applications from the perspective of this concept. Several ideas on how the analysis of ventriloquism might be done in NLP are introduced. [ABSTRACT FROM AUTHOR]

The paper presents the details and the history of building a corpus of instant messenger (chat) conversations with multiple participants used for Computer-Supported Collaborative Learning. Two XML annotation schemas are presented. The first is the basic one in the corpus. It is automatically generated starting from various formats in which chats are exported. The second schema extends the first one. It is generated by an editor for manually annotating the chats. Details of the editor are included. The corpus may be used in different purposes: machine learning, analysis and statistics computation. [ABSTRACT FROM AUTHOR]

Disinformation currently floods the Internet worldwide and likely affects social knowledge building in online communities. Strategic framing in the form of emotional, value, and semantic framing is a common tool fake news producers use to more efficiently disseminate their content, yet these strategies have not been sufficiently examined, even less in relationship with the online dialog initiated by misinformation. In this exploratory study we aim to investigate the most relevant types of strategic framing and their role as predictors of news consumers' emotions, argumentation, and social knowledge building in the online dialog on a German alternative news site. Employing both manual and automated content analysis, we found significant relationships between framing in posted news articles and the subsequent online dialog. News framing predicted negative emotions in the online discussions and interfered with argumentation and social knowledge building. Conclusions pertain to digital information literacy interventions and further research on news framing. • This study combines framing theory and dialogism with disinformation content. • We utilized both manual and automatic content analysis to discover prominent frames in fake news content. • We analyzed roughly 1500 comments from a German fake news site. • Framing predicted social knowledge building in the sense of dialogism and might have led to an echo chamber environment. [ABSTRACT FROM AUTHOR]