Papers with depression detection
When LLMs Meets Acoustic Landmarks: An Efficient Approach to Integrate Speech into Large Language Models for Depression Detection (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are used for depression detection but their application remains unexplored. |
| Approach: | They propose to integrate acoustic speech information into LLMs for depression detection by integrating aural landmarks into the framework. |
| Outcome: | The proposed method adds critical dimensions to speech transcripts and provides insights into the unique speech patterns of individuals. |
Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings (2022.coling-1)
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| Challenge: | Existing black-box-like deep learning methods for depression detection focus on improving classification performance, but it is impossible to explain and interpret those models that rely on state-of-the-art (SOTA) deep learning techniques. |
| Approach: | They propose to use hierarchical attention mechanisms and feed-forward neural networks to encode a model for depression detection on Twitter that leverages metaphorical concept mappings as input. |
| Outcome: | The proposed model leverages metaphorical concept mappings as input to detect depressed individuals and identify features of such users’ tweets. |
Multimodal Topic-Enriched Auxiliary Learning for Depression Detection (2020.coling-main)
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| Challenge: | Existing studies on depression detection rely on textual and visual content to determine whether a human being is depressed or non-depressed. |
| Approach: | They propose a multimodal topic-enriched Auxiliary Learning approach that captures topic information from texts and images for depression detection. |
| Outcome: | The proposed approach improves the performance of the primary task by using topic information from text and images. |
Early Text Classification Using Multi-Resolution Concept Representations (N18-1)
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| Challenge: | e-communications have been misused by cyber-criminals, who hide in the depths of the web. |
| Approach: | They propose a document representation which allows us to generate multiple "views" of the analyzed text. |
| Outcome: | The proposed representation outperforms existing models in two tasks where anticipation is critical: sexual predator detection and depression detection. |
Detecting Depression in Social Media using Fine-Grained Emotions (N19-1)
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| Challenge: | Mental disorders affect millions of people around the world and depression is among the most common. |
| Approach: | They propose a representation of social media documents by a set of emotions generated by lexical resources and subword embeddings. |
| Outcome: | The proposed representation improves the results of the evaluation based on the core emotions and the state-of-the-art representations compared to the current methods. |
Classifying Social Media Users before and after Depression Diagnosis via Their Language Usage: A Dataset and Study (2024.lrec-main)
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| Challenge: | Mental illness can negatively impact individuals’ quality of life as it is considered one of the causes of years lived with disability and it is related to high suicide rates. |
| Approach: | They collect first dataset of textual posts by same users before and after being diagnosed with depression and build multiple predictive models based on Transformers and BERT. |
| Outcome: | The proposed model can be used to detect depression and suicidal thoughts in users who are not diagnosed with depression or suicide. |
An Exploratory Analysis of the Relation between Offensive Language and Mental Health (2021.findings-acl)
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| Challenge: | Using computational models, the use of offensive language is pervasive in social media . a popular line of research is the study of machine learning classifiers to identify offensive content online . |
| Approach: | They analyze social media posts written by individuals with depression and those without . they train computational models to compare use of offensive language with depression detection . |
| Outcome: | The proposed models show that offensive language is more frequently used in the samples written by individuals with depression and those showing signs of depression. |
Explainable Depression Detection in Clinical Interviews with Personalized Retrieval-Augmented Generation (2025.findings-acl)
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| Challenge: | Existing systems rely on black-box neural networks, which lack interpretability, which is crucial in mental health contexts. |
| Approach: | They propose a Retrieval-augmented generation framework for Explainable depression detection that retrieves evidence from clinical interview transcripts, providing explanations for predictions. |
| Outcome: | The proposed framework retrieves evidence from clinical interview transcripts, providing explanations for predictions. |
SpeechT-RAG: Reliable Depression Detection in LLMs with Retrieval-Augmented Generation Using Speech Timing Information (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have been extensively utilized for health-related tasks, yet their performance in depression detection remains limited when relying solely on text input. |
| Approach: | They propose a system that leverages speech timing features for depression detection and reliable confidence estimation. |
| Outcome: | The proposed system outperforms text-based RAG systems in depression detection and confidence estimation. |
Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires (2022.acl-long)
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| Challenge: | Existing approaches to identify mental health conditions using social media are limited by the presence of symptoms described in a questionnaire used by clinicians. |
| Approach: | They propose to ground a model in PHQ9's symptoms to improve generalization . they also show that this approach can still perform competitively on in-domain data. |
| Outcome: | The proposed approach can perform competitively on in-domain data while improving generalizability and generalisability. |
Leveraging Mental Health Forums for User-level Depression Detection on Social Media (2022.lrec-1)
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| Challenge: | Existing methods to detect depression on social media platforms are limited due to the vastness of social media content and the lack of linguistic features. |
| Approach: | They propose to optimize the performance of user-level depression classification to lessen the burden on computational resources. |
| Outcome: | The proposed system outperforms baselines across standard metrics for the task of depression detection in text. |
Mitigating Interviewer Bias in Multimodal Depression Detection: An Approach with Adversarial Learning and Contextual Positional Encoding (2025.findings-emnlp)
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| Challenge: | Clinical interviews are a standard method for assessing depression . however, these methods neglect the broader conversational context . |
| Approach: | They develop a multimodal dialogue-level transformer that captures the dynamics of dialogue within each interview . they also build an adversarial classifier with a gradient reversal layer to learn shared representations . |
| Outcome: | The proposed model captures the dynamics of dialogue within each interview using positional embedding and question context vectors. |
Predicting Depression in Screening Interviews from Interactive Multi-Theme Collaboration (2025.findings-acl)
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| Challenge: | Existing methods for depression detection do not capture intra-theme and inter-themes correlation and do not allow clinicians to focus on themes of interest. |
| Approach: | They propose an interactive depression detection framework that leverages in-context learning techniques to identify themes in clinical interviews and then models both intra-theme and inter-themes correlation. |
| Outcome: | The proposed framework achieves 12% on Recall and 35% on F1-dep. metrics compared to the previous state-of-the-art model on the depression detection dataset DAIC-WOZ. |
ReDepress: A Cognitive Framework for Detecting Depression Relapse from Social Media (2025.emnlp-main)
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Aakash Kumar Agarwal, Saprativa Bhattacharjee, Mauli Rastogi, Jemima S. Jacob, Biplab Banerjee, Rashmi Gupta, Pushpak Bhattacharyya
| Challenge: | Almost 50% of depression patients face the risk of going into relapse. |
| Approach: | They propose to validate a social media dataset on depression relapse using cognitive theories of depression. |
| Outcome: | The first clinically validated social media dataset focused on depression relapse comprises 204 Reddit users annotated by mental health professionals. |
FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data (2026.acl-long)
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| Challenge: | Social media text data is often used to train machine learning models to identify users exhibiting high-risk mental health behaviors. |
| Approach: | They apply federatedlearning and Differentially Private FL to two widely-studied mental health prediction tasks using social media text data. |
| Outcome: | The proposed methods achieve comparable performance to centralized training on depression identification, but have a large performance-privacy trade-off even with low levels of noise. |