Cognitive-Linguistic Indicators of Depression in Online Communities: Analysed by DistilBERT and Holographic Reduced Representation

This paper investigates whether combining cognitively grounded linguistic features with transformer-based embeddings improves automated detection of depression in online text. The study compares a TF-IDF baseline model with a hybrid DistilBERT HRR model, showing the latter achieves a significantly higher macro F1 score of 0.94.