Corpus anotado da expressão metapragmática com todo respeito no X/Twitter: configurações discursivas e funções pragmáticas
Abstract
This dataset comprises 100 records of the Portuguese metapragmatic expression com todo respeito (“with all due respect”), collected from X (formerly Twitter). The corpus encompasses different contexts of digital interaction in Portuguese and provides linguistic annotations designed to investigate the discourse configurations and pragmatic functions associated with this expression. The data are systematically organized according to categories covering the context of production, publication dates and URLs, the position of the expression within the utterance, its combination with the adversative connector mas (“but”), the construction of the discourse target, the predicative pressure of the subsequent segment, and the identified discourse-pragmatic configuration. The annotated configurations include contrastive attacks, evaluations framed by appropriateness, credential-based contestation, categorical norms, and uses in which the expression itself becomes the object of metapragmatic commentary. The organization of the corpus makes it possible to examine how the expression com todo respeito contributes to the negotiation of interpersonal relationships, the formulation of evaluations, and the introduction of potentially conflicting positions in digital interactions. This dataset served as the empirical basis for a research article co-authored with Professor Marcos Luiz Wiedemer, PhD, scheduled for publication in the journal Linguagem em Foco. Making this dataset openly available aims to promote methodological transparency, reproducibility, and further research on metapragmatics, linguistic constructions, and discourse practices in digital environments.
Fields of Study & Subjects
Research Data Management Practices
Data Analysis and Archiving
Information Science and Libraries
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 27 February 2026
- Language
- EN
- Document type
- Dataset