Modern Greek seminar cycle: Stance identification in Modern Greek texts from Twitter/X: a corpus-based study

Speaker: Vasiliki Simaki, Lund University

Stance has been extensively studied in high-resource languages such as English. However, despite the availability of linguistic resources for Modern Greek, research on stance identification and analysis in Greek data remains limited. This study (in progress) aims to address this gap by applying a cognitive-functional framework for stance—originally developed and tested on English social media texts—to Modern Greek data. In this study, a corpus-based approach is used, and six frequent stance categories are identified and codified. The data for this study are drawn from Twitter/X, providing a rich source of informal, user-generated content. The primary objective was to explore whether a framework designed for English can be effectively applied to a different linguistic and cultural context. In addition to identifying the most frequent stance categories in Modern Greek tweets, the presence of stance markers and other relevant constructions used to express these stances is observed. This allows for a comparative analysis between English and Modern Greek, shedding light on cross-linguistic similarities and differences in expressing stance in language. Such a comparison not only tests the adaptability of the current framework but also contributes to a broader understanding of stance as a linguistic and social phenomenon across languages.