cs.CLApr 15, 2026

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

Authors: Brian Van Steen

Organizations: School of Computing, University of Leeds

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Abstract

This paper investigates whether combining cognitively grounded linguistic features with transformer-based embeddings improves automated detection of depression in online text. Using Beck's Cognitive Theory of Depression, the study extracts cognitive distortions as measurable features, including first-person pronoun density, absolutist words, and negative emotion in Reddit posts from depression-related and control communities. Using a subset of the Kaggle Reddit Suicide and Depression Detection dataset, two classification pipelines are compared, a TF-IDF embedding with Naive Bayes as a baseline, and a hybrid model that concatenates DistilBERT sentence embeddings with Holographic Reduced Representation (HRR) vectors encoding the cognitive-linguistic features, followed by Logistic Regression. The hybrid DistilBERT HRR model achieves a macro F1 score of 0.94 versus 0.80 for the TD-IDF baseline, with 5-fold cross validation F1 improving from 0.83 to 0.92, and AUC from 0.958 to 0.981.

Figures & tables

Figure 1: Confusion matrix for the TF-IDF + Naïve Bayes baseline, achieving macro F 1 of 0.80 and 5-fold CV F 1 of 0.83.
Figure 2: Confusion matrix for the DistilBERT ⊕\oplus HRR + Logistic Regression hybrid model, achieving macro F 1 of 0.94 and 5-fold CV F 1 of 0.92.
Figure 3: ROC curves comparing TF-IDF + NB (AUC = 0.958) and DistilBERT ⊕\oplus HRR + LR (AUC = 0.981). The hybrid model shows improved sensitivity in the top-left region.
Figure 4: Precision–Recall curves. The hybrid model maintains higher precision as recall approaches 1.0, whereas the baseline model decreases earlier.
Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 5: Connected Papers visualisation showing the citation network surrounding key references used in the literature review.
Figure 6: NLTK LookupError for missing punkt_tab resource.
Figure 7: Impact of TF-IDF vocabulary control on classification performance.
Figure 8: Distribution of token lengths showing disproportionate truncation of depression-class posts at the 512-token limit.

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