A Corpus-Based Study of Adjectives and Collocates in Reddit Posts on Anxiety and Depression - Yang Yuhan; Su Kia Lau; Handoko Handoko; Leng Lee Yap; Sheena Kaur (Jurnal Arbitrer, 2026)
Talking openly about anxiety and depression (A&D) remains difficult for many people because of the stigma surrounding mental illness. Anonymous online platforms such as Reddit provide a space where users can express their thoughts and emotions more freely. This study considers how individuals linguistically construct and intensify emotional distress by examining (1) the adjectives used to express A&D, (2) the content-word collocates that co-occur with these adjectives, (3) the lexical features of these collocates, and (4) the emotional meanings conveyed through these collocational patterns. The dataset consisted of 1,440 Reddit posts (approximately 300,000 tokens) systematically sampled from the r/Anxiety and r/Depression subreddits between 2023 and 2025. An observational mixed-methods corpus linguistic approach was used to examine the data. Quantitative corpus linguistic analyses were carried out using AntConc, including frequency profiling and Mutual Information (MI) analysis, and were enhanced by qualitative concordance and Keyword-in-Context (KWIC) analysis to examine collocational patterns in context. The analysis shows a predominance of negatively valenced adjectives (e.g., anxious, depressed, hopeless, and suicidal), whose meanings are systematically intensified through their collocational environments. The collocates show distinctive lexical features. These include clinical nouns, linking and change-of-state verbs, and degree and frequency adverbs. These features construct varying levels of affective intensity and psychological distress. Emotional meaning is encoded in recurrent collocational patterns. Individual lexical items reveal only part of this meaning. This shows the value of collocational analysis for digital mental health research. The findings also possess practical implications. They may help improve the diagnostic sensitivity of automated digital mental health tools and foster more empathetic clinical communication.