K-SENSE: A Knowledge-Guided Self-Augmented Encoder for Neuro-Semantic Evaluation of Mental Health Conditions on Social Media
The early detection of mental health conditions, such as stress and depression, through social media text is a significant challenge in the fields of computational psychiatry and natural language processing. As social media platforms continue to grow, the need for automated systems that can accurately identify mental health issues becomes increasingly urgent. Existing methods often struggle with the complexities of figurative language, implicit emotional expression, and the inherent noise that comes with user-generated content. In response to these challenges, a novel framework known as K-SENSE has been developed to enhance mental health condition evaluation using social media data.
Introduction to K-SENSE
K-SENSE, which stands for Knowledge-guided Self-augmented Encoder for Neuro-Semantic Evaluation of Mental Health, is designed to integrate external psychological reasoning with robust internal representations. This framework is unique in that it combines two approaches that have previously been used in isolation: leveraging commonsense knowledge and employing self-augmentation through contrastive learning.
Key Features of K-SENSE
The K-SENSE framework operates through a three-stage encoding pipeline:
- Commonsense Knowledge Extraction: The first stage involves extracting inferential commonsense knowledge using the COMET model, which focuses on five distinct mental state dimensions. This step is crucial for understanding the context and implications behind social media text.
- Semantic Anchor Construction: In the second stage, a semantic anchor is built by combining hidden representations from two parallel encoding streams. These representations are then projected into a shared space before fusion, creating a consolidated understanding of the data.
- Supervised Contrastive Learning: The final stage implements a supervised contrastive learning objective. This aligns representations of the same class, enhancing the model’s ability to discern relevant information and suppress irrelevant knowledge noise.
Performance Evaluation
K-SENSE has been evaluated using two datasets: Dreaddit for stress detection and Depression_Mixed for depression detection. The results are promising, with K-SENSE achieving mean F1-scores of 86.1% (a 0.6% improvement) for stress detection and 94.3% (a 0.8% improvement) for depression detection across five independent runs. These scores demonstrate significant advancements, with improvements of approximately 2.6 and 1.5 percentage points over the strongest prior baselines, respectively.
Ablation Studies
Ablation experiments conducted during the evaluation confirm the contributions of each architectural component within K-SENSE. Notably, the temporal knowledge integration strategy and the decision to keep the knowledge encoder frozen during fine-tuning were found to be critical in enhancing the model’s performance.
Conclusion
The K-SENSE framework represents a significant step forward in the automated evaluation of mental health conditions through social media analysis. By effectively combining external knowledge with robust internal representations, K-SENSE stands to improve the accuracy of mental health assessments, ultimately contributing to better mental health outcomes for individuals seeking help through online platforms. As technology continues to evolve, the integration of advanced AI models like K-SENSE will play an essential role in the future of mental health care.
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