Coding Depression in the Digital Realm: An Analysis via Systemic Functional Linguistics and Social Semiotics

This study investigates the language encoding of emotional states by Turkish Twitter users through the hashtag #depresyondayım ("I am depressed"), utilizing Martin and White's (2005) Appraisal Framework within Halliday's (1978) Social Semiotic perspective. This research utilizes Systemic Functional Linguistics to examine how users employ attitudinal resources, engagement methods, and graduation procedures as semiotic tools to articulate emotional pain, negotiate social roles, and create identities in digital environments. A qualitative analysis of 130 tweets indicates that the content primarily consists of affect resources (81.5%), monoglossic engagement (84.6%), and force-based graduation (67.7%). Notable linguistic patterns were identified across seven thematic categories: everyday frustrations, economic hardships, emotional isolation, academic/work stress, seasonal effects, societal issues, and social deprivation. The research illustrates how the Appraisal Framework elucidates the intricate relationship among emotion, evaluation, and intensification in digital communication, while exposing how depression discourse operates as a multifaceted semiotic resource fulfilling diverse social functions beyond the expression of clinical distress. This study aims to contribute to digital discourse analysis by applying the Appraisal Framework to Turkish social media texts. In the process, observations were made about the role of Turkish morphological and syntactic features in evaluative language use. Keywords: Appraisal Framework, Systemic Functional Linguistics, social media discourse, hashtags, linguistic identity, attitude, affect, engagement