Cognitive-Linguistic Indicators of Depression in Online Communities: Analysed by DistilBERT and Holographic Reduced Representation
arXiv:2606.00026v1 Announce Type: new 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
The proliferation of online textual data and advancements in transformer models enable more sophisticated analysis of human language for health insights.
Automated detection of mental health indicators in online communities offers new avenues for early intervention and public health monitoring, especially as AI models become more adept at nuanced linguistic analysis.
The specificity and accuracy of AI models in identifying depression signals based on cognitive-linguistic features are improving, offering more finely-tuned tools for mental health assessment.
- · Mental Health Organizations
- · Social Media Platforms (with responsible AI use)
- · AI-powered health tech companies
- · Individuals seeking early mental health support
- · Manual mental health screening
- · Platforms without robust ethical AI guidelines
Improved early detection of depression through AI analysis of online text.
Development of proactive mental health support systems integrated into online platforms.
Enhanced understanding of the linguistic manifestations of various mental health conditions, leading to more personalized therapeutic approaches.
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