Argumentation schemes, fallacies, and evidence in politicians' argumentative tweets-A coded dataset.

This coded database presents a corpus of argumentative tweets published by four politicians (Matteo Salvini, Donald Trump, Jair Bolsonaro, and Joe Biden) within 6 months from their taking office, which corresponds to the official end of their election campaign. The coding is based on a threefold method of analysis based on the instruments of argumentation theory and pragmatics. First, the types of arguments are recognized and classified according to a systematic organization of the argumentation schemes developed in the literature. Second, arguments are evaluated considering the fallacies committed. Third, the uses and misuses of "emotive words" are assessed. Based on this theoretical framework, each tweet is thus attributed three categories of codes: 1) argument types (maximum two, corresponding to the most important ones); 2) fallacies (maximum two types of fallacies, plus a distinct indication of the lack of necessary evidence or false presupposition); and 3) emotive language (maximum three emotive words, plus the most important emotion expressed). A total of 2657 tweets are coded, providing a ground for comparative works and an instrument for training further coding of different corpora.

Multicultural classroom discourse dataset on teachers' and students' dialogic empathy.

This dataset belongs to a large-scale educational intervention project introducing cultural literacy learning in schools. The dataset includes transcribed dialogic interactions from 111 lessons conducted in classrooms of five countries: England, Portugal, Germany, Spain, and Cyprus. The lessons were part of the same Cultural Literacy Learning Programme made available to teachers from three age groups (pre-primary, primary, secondary) to implement with their ordinary classes during ten sessions. The data are from the third and eighth sessions and followed the same structure, content, and objectives. As the main goal of the project to which the dataset belongs was on cultural literacy learning, understood as dialogic dispositions and values, the data were coded according to a Dialogic Empathy coding scheme presented in detail in Macagno et al. (2020). This rich multicultural, and multilinguistic, dataset is offered for further analysis by different types of researchers, such as linguists interested in intercultural pragmatics or educational psychologists interested in cross-sectional studies of dialogue and reasoning skills.

Coding Problematic Understanding in Patient-provider Interactions.

This article proposes a coding scheme for identifying and assessing linguistic evidence of problematic understanding in health-care provider communication with patients affected by type 2 diabetes mellitus. Drawing on the existing literature in pragmatics and linguistics, the scheme is grounded on the distinctions between the different types of linguistic evidence of the occurrence of a misunderstanding or a problematic understanding, divided into three levels (stronger, acceptable and weak) based on their probative force. The application of the scheme is illustrated through a pilot study, conducted on an Italian corpus of 46 transcripts of videotaped consultations between six health-care providers and 13 patients affected by diabetes mellitus type 2. The most frequent types of linguistic evidence of problematic understanding were the categories of "acceptable" (amounting to 58% of the total) and the "strong" evidence (35%). Patients' problematic understanding was detected to occur significantly more frequently than health-care providers. Providers were also found to be significantly more aware of possible misunderstandings, tending to verify more frequently the correctness of their own interpretations. This pilot study represents a first step in the process of developing a productive evidence-based tool for detecting problematic understanding, which can be used for implementing linguistic strategies for helping prevent the risk of misunderstandings in health-care communication. Our findings show that misunderstandings are widespread between patients and that some linguistic strategies may be more effective than others in preventing the risk of misunderstandings, suggesting possible directions of research for improving health-care providers' communicative skills.